m.sc. mechatronics (po 2014)

M.Sc. Mechatronics(PO 2014)RoboticsDate: 01.09.2021

Study Area Mechatronic Systems

Module manual: M.Sc. Mechatronics (PO 2014)Robotics

Date: 01.09.2021

Study Area Mechatronic SystemsEmail: [email protected]

I

Contents

1 Fundamentals 1

1.1 Micro-technical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Electromechanical Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Microsystem Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4System Dynamics and Automatic Control Systems III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Advanced Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 More Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Tools and Methods in Product Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Real Time Applications and Communication with Microcontrollers and programmable Logic Devices 9System Dynamics and Automatic Control Systems II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Digital Control Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Optionals in Technical and Natural Science 14

2.1 Optionals ETiT, MPE, CS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1.1 Basics Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Foundations of Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Foundations of Robotics for Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.1.2 Additionals Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.1.2.1 A: Sensors and Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Actuator Materials and Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Computer Vision in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Computer Vision I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Computer Vision II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Digital Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Fundamentals of Adaptronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Sensor Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Sensor Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.1.2.2 B: Systems and Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Introduction to Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Electromechanical Systems I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Design of Human-Machine-Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Mechatronic Systems Engineering I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Machine Learning and Deep Learning for Automation Systems . . . . . . . . . . . . . . . . . . . 34Machine Learning for Robotics & Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Machine Learning Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Mechatronic Systems Engineering II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Human-mechatronics systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Robotics in industry: Basics and application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Optimal and Predictive Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Automated Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452.1.2.3 C: Methodological competences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Manufacturing Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Digital Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Digital Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

II

Real-Time Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Machine Design I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Machine Design II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Fuzzy Logic, Neural Networks and Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . 53Fundamentals of Navigation I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Fundamentals of Navigation II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Advanced Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Identification of Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Industrial Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Controller Design for Multivariable Systems in State Space . . . . . . . . . . . . . . . . . . . . . . 61Software Engineering - Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Software-Engineering - Maintenance and Quality Assurance . . . . . . . . . . . . . . . . . . . . . 63

2.2 ADP / Seminars, Labs, CS-ES-NS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642.2.1 ADP / Seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.2.1.1 Projekt Seminars Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Project Seminar Learning Robots for Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Robotics Project Seminar for Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Project Seminar Robotics and Computational Intelligence . . . . . . . . . . . . . . . . . . . . . . . 662.2.1.2 Further Projekt Seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67ADP (6 CP) Applied Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67ADP (6 CP) Dynamics and Vibrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68ADP (6 CP) Automotive Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69ADP (6 CP) Mechatronic Systems in Mechanical Engineering . . . . . . . . . . . . . . . . . . . . 70ADP (6 CP) Product Development and Machine Elements . . . . . . . . . . . . . . . . . . . . . . . 71ADP (6 CP) System Reliability, Adaptive Structures and Machine Acoustics . . . . . . . . . . . . 72ADP (6 CP) Internal Combustion Engines and Powertrain Systems . . . . . . . . . . . . . . . . . 73Application, Simulation and Control of Power Electronic Systems . . . . . . . . . . . . . . . . . . 74Product Development Methodology I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Planning and Application of Electrical Drives (Drives for Electric Vehicles) . . . . . . . . . . . . 76Project Seminar Automatic Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Autonomous Driving Lab I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Energy Converters and Electric Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Project seminar Advanced Applications of Lighting Engineering . . . . . . . . . . . . . . . . . . . 81Project seminar Applications of Lighting Engineering . . . . . . . . . . . . . . . . . . . . . . . . . 82Project Seminar MFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Multimedia Communications Project I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Project Course Practical Application of Mechatronics . . . . . . . . . . . . . . . . . . . . . . . . . . 86Project Course Control Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Multimedia Communications Seminar I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Seminar Software System Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

2.2.2 Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Advanced Integrated Circuit Design Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Practical Training with Drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Mechatronics Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Electromechanical Systems Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Laboratory Matlab/Simulink II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94Multimedia Communications Lab I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Laboratory Control Engineering II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Software Lab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Tutorial Introduction to Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Tutorial Automotive Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Tutorial on Flight Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Tutorial Advanced Cax Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Tutorial Basic Robot Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Tutorial Machine Acoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Contents III

Tutorial Pneumatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052.2.3 CS-ES-NS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Basics of Economics for Engineers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Introduction to Numerical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Electric drives for cars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108Matrix Analysis and Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Mechatronics Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Optimization of static and dynamic systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Laboratory Matlab/Simulink II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Laboratory Control Engineering II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Autonomous Driving Lab II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Robust Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Fundamentals of Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Contents IV

1 Fundamentals

1.1 Micro-technical Systems

Module nameElectromechanical Systems I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kn-1050 5 CP 150 h 90 h 1 WiSe

Language Module ownerGerman Prof. Dr. Mario Kupnik

1 ContentStructure and design methods of elektromechanical systems, mechanical, acoustical and thermal networks,transducers between mechanical and acoustical networks. Design and devices of electromechanical trans-ducers.

2 Learning objectives / Learning OutcomesComprehension, description, calculation and application of the most relevant electromechanical transduc-ers, comprising electrostatic transducer (e.g. microphone and accelerometer), piezoelectric transducers(e.g micro motors, micro sensors), electrodynamic transducer (loudspeaker, shaker), piezomagnetic trans-ducer (e.g. ultrasonic source). Design of complex electromechanical systems like sensors and actuatorsand their applications by applying the discrete element network method.

3 Recommended prerequisite for participationElectrical Engineering and Information Technology I

4 Form of examinationModule Final Examination:

• Module Examination (Technical Examination, Optional, Standard Grading System)

5 GradingModule Final Examination:

• Module Examination (Technical Examination, Optional, Weighting: 100 %)

6 Usability of this moduleBSc ETiT, BSc WI-ETiT, MSc MEC

7 Grade bonus compliant to §25 (2)

8 ReferencesBook: Electromechanical Systems in Microtechnic und Mechatronic, Springer 2012, Script for lecture Elec-tromechanical Systems I, Workbook

Courses

Course Nr. Course name18-kn-1050-vl Electromechanical Systems I

Instructor Type SWSProf. Dr. Mario Kupnik, Prof. Dr. techn. Dr.h.c. Andreas Binder, M.Sc. OmarBen Dali

Lecture 2

1

Course Nr. Course name18-kn-1050-ue Electromechanical Systems I


Practice 2

1.1 Micro-technical Systems 2

Module nameMicrosystem Technology

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bu-2010 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Ph.D. Thomas Peter Burg

1 ContentIntroduction and definitions to micro system technology; definitions, basic aspects of materials in microsystem technology, basic principles of micro fabrication technologies, functional elements of microsystems,micro actuators, micro fluidic systems, micro sensors, integrated sensor-actuator systems, trends, economicaspects.

2 Learning objectives / Learning OutcomesTo explain the structure, function and fabrication processes of microsystems, including micro sensors,micro actuators, micro fluidic and micro-optic components, to explain fundamentals of material properties,to calculate simple microsystems.

3 Recommended prerequisite for participationBSc


• Module Examination (Technical Examination, Written Examination, Duration: 90 min, StandardGrading System)


• Module Examination (Technical Examination, Written Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc MEC, MSc WI-ETiT, MSc Medizintechnik


8 ReferencesScript for lecture: Mikrosystemtechnik

Courses

Course Nr. Course name18-bu-2010-vl Microsystem Technology

Instructor Type SWSProf. Ph.D. Thomas Peter Burg Lecture 2

Course Nr. Course name18-bu-2010-ue Microsystem Technology

Instructor Type SWSProf. Ph.D. Thomas Peter Burg Practice 1

1.1 Micro-technical Systems 3

1.2 Dynamic Systems

Module nameSystem Dynamics and Automatic Control Systems III

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ad-2010 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Jürgen Adamy

1 ContentTopics covered are:

• basic properties of non-linear systems,• limit cycles and stability criteria,• non-linear control of linear systems,• non-linear control of non-linear systems,• observer design for non-linear systems

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of:

• explaining the fundamental differences between linear and non-linear systems,• testing non-linear systems for limit cycles,• stating different definitions of stability and testing the stability of equilibria,• recalling the pros and cons of non-linear controllers for linear systems,• recalling and applying different techniques for controller design for non-linear systems,• designing observers for non-linear systems

3 Recommended prerequisite for participation





6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Systemdynamik und Regelungstechnik III (available for purchase at the FG office)

Courses

Course Nr. Course name18-ad-2010-vl System Dynamics and Automatic Control Systems III

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Lecture 2

Course Nr. Course name18-ad-2010-ue System Dynamics and Automatic Control Systems III

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Practice 1

1.2 Dynamic Systems 4

Module nameAdvanced Dynamics

Module Nr. Credit Points Workload Self study Duration Cycle offered16-25-5060 6 CP 180 h 105 h 1 WiSe

Language Module ownerGerman Prof. Dr.-Ing. Bernhard Schweizer

1 ContentIntroduction and definition of multibody systems.Kinematics of rigid bodies; spatial motion (translation and rotation).Formulation of constraint equations (scleronomic, rheonomic, holonomic and nonholonomic constraints);definition of generalized coordinates and virtual displacements.Kinematics of multibody systems; tree-structured systems and systems with closed loops; description ofspatial systems using absolute coordinates and relative coordinates.Kinetics of multibody systems; Newton´s law and Euler´s law; formulation of the equations of motionusing absolute coordinates (Index-3, Index-2 and Index-1 formulations) and relative coordinates.Principle of d´Alembert, principle of virtual power, Lagrange´s equations of the second kind, etc.Linearization of the equations of motion; theory for linear systems with constant coefficients.Applicationexamples: automotive engineering, robotics, gear mechanisms, engine dynamics, rotor dynamics, etc.

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:

• Mathematically describe the spatial motion of a rigid body.• Describe the kinematics of complex planar and spatial dynamical systems.• Derive the equations of motion for complex planar and spatial systems using the Newton-Euler equa-

tions.• Applying the principles of mechanics in order to derive the governing equations of motion (as an

alternative to the Newton-Euler equations).• To generate suitable mathematical models for machines, engines and mechanisms in order to

calculate the motion of the system and the forces/torques acting on the bodies.

3 Recommended prerequisite for participationTechnical Mechanics I to III (Statics, Elastomechanics, Dynamics) and Mathematics I to III recommend.


• Module Examination (Technical Examination, Written Examination, Standard Grading System)Written exam 150 min



6 Usability of this moduleMaster MPE PflichtWI/MB, Master Mechatronik


8 ReferencesWoernle, C.: „Mehrkörpersysteme“, Springer, 2011.Shabana, A.: „Dynamics of Multibody Systems”, Cambridge University Press, Third Edition, 2010.Haug, E.J.: „Computer-Aided Kinematics and Dynamics of Mechanical Systems“, Allyn and Bacon, 1989.Markert, R.: „Strukturdynamik“, Shaker, 2013.Dresig, H.; Holzweißig, F.: „Maschinendynamik”, 10. Au-flage, Springer, 2011.

Courses


Course Nr. Course name16-98-4094-vl Machine Dynamics

Instructor Type SWSLecture 3

Course Nr. Course name16-98-4094-hü Advanced Dynamics

Instructor Type SWSLecture HallPractice

2

Course Nr. Course name16-25-5060-gü Advanced Machine Dynamics

Instructor Type SWSGroup Practice 0


1.3 More Fundamentals

Module nameTools and Methods in Product Development


Language Module ownerGerman Prof. Dr.-Ing. Eckhard Kirchner

1 ContentBasics of product development and structuring of the development process. Clarification of the task andrequirement list, basics of development of new products, basics of management of product costs by re-ducing of manufacturing costs, value analysis and targeted costing; Development of environmentally safeproducts, development of products and product structures designed for variety; Basics of safety technologyand development of products designed for safety; Failure and weak-point analysis; Utilizing Prototypes;Development and Production in a globalized world.


• Analyse design tasks by questioning them specifically to identify targets and central issues of thedesign task. The students are also able to translate customer’s wishes into product requirements andassess the requirement’s importance.

• Create a formal description of the design task by generating a list of requirements. The students arealso able to differentiate between customer’s wishes and requirements.

• Describe principles, advantages, and limits of simultaneous engineering and explain its relevanceand impact for practical work.

• Denominate and describe the approach and the tasks of developing a new product, using a mor-phological analysis and systematic combination of solutions, as well as being able to explain theirrelevance in innovation projects.

• Explain the principles of Total Quality Management and their implementation and relevance in com-panies. The students are also able to use FMEA as a preventive failure avoidance method.

• Differentiate the basic wording for development of products designed to security and explain theprinciples of design to security regarding their effectiveness for specific tasks and use them to developimproved products.

• Differentiate the main strategies of product cost management and knowing the basics of their genesisover the product’s lifecycle. The students should also be able to analyse cost structures using break-even-analysis, function costing and draft strategies and actions to reach the target costs and evaluatethose strategies in regard to their reach.

• Explain the approach and tasks of creating an ecobalance.• Analyse companies’ situations regarding the variety of products and identify and explain the danger

that comes from complexity.• Explain and evaluate limits of applicability of prototypes.• List the challenges of development and production in globally acting enterprises and to identify

alleviating measures.

3 Recommended prerequisite for participationNone


• Module Examination (Technical Examination, Optional, Standard Grading System)Written exam 90 min or oral exam 30 min



1.3 More Fundamentals 7

6 Usability of this moduleWP Bachelor MPE


8 ReferencesU. Lindemann. Methodische Entwicklung technischer Produkte: Methoden flexibel und situationsgerechtanwenden. VDI-Buch. Springer-Verlag Berlin Heidelberg, 2009.G. Pahl;W. Beitz; J. Feldhusen; K.H. Grote. Konstruktionslehre – Grundlagen erfolgreicher Produktentwick-lung, Methoden und Anwendungen. Springer Verlag, Berlin, 2006.E. Kirchner & H. Birkhofer. Werkzeuge und Methoden der Produktentwicklung, Vorlesungsunterlagen despmd, 2018

Courses

Course Nr. Course name16-05-5080-vl Tools and Methods in Product Development


Course Nr. Course name16-05-5080-ue Tools and Methods in Product Development

Instructor Type SWSPractice 2


Module nameReal Time Applications and Communication with Microcontrollers and programmable Logic Devices

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2040 4 CP 120 h 75 h 1 WiSe/SoSe

Language Module ownerGerman Prof. Dr.-Ing. Gerd Griepentrog

1 ContentMicrocontroller and programmable logic devices are being used for a variety of control tasks for industrialand residential products and systems. For the control of drives and power electronics, those devices areused for the control of frequency converters or DC/DC converters.In most of these applications, real time requirements have to be met. Simultaneously a communicationinterface has to be served.The module will impart knowledge and expertise on how to realize successfully control task.More in detail, the following content will be taught:

• Architecture of microcontroller• Structure and function of FPGAs, tools and programming languages• Typical peripheral components for microcontrollers• Capture & Compare, PWM, A/D-converter• I2C, SPI, CAN, Ethernet• Programming of microcontrollers in C• Software: real-time properties, interrupt handling, interrupt latency• Control of inductive components• Basic of circuit design for power electronics, Power-MOSFETS, IGBTsNumerical methods

2 Learning objectives / Learning OutcomesStudents will be able to:

• Separate a digital control task into HW and SW parts• Specify the HW-content in a HW description language and implement the SW by means of a micro-

controller• Evaluate the real-time capabilities of a program and to determine upper limits for the response time

of the systemTransfer the developed solution to the target system by means of a development kitand debug the software onto the target system.

3 Recommended prerequisite for participationBasic knowledge in programmig language C (syntax, operators, pointer)





6 Usability of this moduleMSc MEC, MSc ETiT


8 ReferencesScript, Instruction for practical lab courses, ppt-Slides; either in hard-copy or for download; User Manualsof the used devices and development kits

Courses


Course Nr. Course name18-gt-2040-vl Real Time Applications and Communication with Microcontrollers and programmable

Logic Devices

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog Lecture 1

Course Nr. Course name18-gt-2040-pr Real Time Applications and Communication with Microcontrollers and programmable

Logic Devices

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog, Prof. Dr.-Ing. Christian Hochberger Internship 2


Module nameSystem Dynamics and Automatic Control Systems II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ad-1010 7 CP 210 h 135 h 1 SoSe


1 ContentMain topics covered are:

• Root locus method (construction and application),• State space representation of linear systems (representation, time solution, controllability, observ-

ability, observer- based controller design)


• constructing and evaluating the root locus of given systems• describing the concept and importance of the state space for linear systems• defining controllability and observability for linear systems and being able to test given systems with

respect to these properties• stating controller design methods using the state space, and applying them to given systems• applying the method of linearization to non-linear systems with respect to a given operating point

3 Recommended prerequisite for participationSystem Dynamics and Control Systems I





6 Usability of this moduleBSc ETiT, MSc MEC, MSc iST, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Systemdynamik und Regelungstechnik II, Shaker Verlag (available for purchase at the FG office)

Courses

Course Nr. Course name18-ad-1010-vl System Dynamics and Automatic Control Systems II


Course Nr. Course name18-ad-1010-ue System Dynamics and Automatic Control Systems II



Module nameDigital Control Systems I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-2020 4 CP 120 h 75 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Ulrich Konigorski

1 ContentTheoretical fundamentals of sampled control systems:Discrete-time functions, sample/hold element, z-transform, convolution sum, z-transfer function, stabilityof sampled systems, design of digital controllers, discrete PI-, PD-, and PID-controllers, compensation anddead-beat controller, anti-windup methods

2 Learning objectives / Learning OutcomesThe students know the fundamental analysis and design methods for digital feed-forward and feed-backcontrol systems. They know the fundamental differences between continuous-time and discrete-time con-trol systems and can design and analyze discrete-time control systems using different methods.

3 Recommended prerequisite for participationHelpful is knowledge of the Laplace- and Fourier-transforms as well as continuous-time control systems.These fundamentals are taught in the lecture “System Dynamics and Control Systems I”





6 Usability of this moduleBSc/MSc Wi-ETiT, MSc ETiT, BSc/MSc CE, MSc MEC, BSc/MSc iST, MSc iCE, MSc Informatik


8 ReferencesLecture notes Konigorski: “Digitale Regelungssysteme”Ackermann: "Abtastregelung"Aström, Wittenmark: "Computer-controlled Systems"Föllinger: "Lineare Abtastsysteme"Phillips, Nagle: "Digital control systems analysis and design"Unbehauen: "Regelungstechnik 2: Zustandsregelungen, digitale und nichtlineare Regelsysteme"

Courses

Course Nr. Course name18-ko-2020-vl Digital Control Systems I

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Lecture 2

Course Nr. Course name18-ko-2020-ue Digital Control Systems I

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Practice 1


Module nameModeling and Simulation



1 Contentaim of modeling, theoretical modeling by application of fundamental physical laws, generalized networkanalysis, modeling of distributed parameter systems, model reduction, linearization, order reduction, digi-tal simulation of linear systems, numerical integration methods

2 Learning objectives / Learning OutcomesThe students will know different techniques for the mathematical modeling of dynamic systems from var-ious domains. They will acquire the ability to digitally simulate the dynamic behavior of the modeledsystems and to systematically apply the available numerical integration methods.

3 Recommended prerequisite for participationBasic knowledge of continuous- and discrete-time control theory. Supplementary lectures are “SystemDynamics and Control Systems I and II” as well as “Digital Control Systems I and II”.





6 Usability of this moduleMSc ETiT, MSc MEC


8 ReferencesLecture notes Konigorski: “Modellbildung und Simulation”,Lunze: „Regelungstechnik 1 und 2“,Föllinger: „Regelungstechnik: Einführung in die Methoden und ihre Anwendung“

Courses

Course Nr. Course name18-ko-2010-vl Modeling and Simulation

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Lecture 2

Course Nr. Course name18-ko-2010-ue Modeling and Simulation

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Practice 1


2 Optionals in Technical and Natural Science

2.1 Optionals ETiT, MPE, CS

2.1.1 Basics Robotics

Module nameFoundations of Robotics

Module Nr. Credit Points Workload Self study Duration Cycle offered20-00-0735 10 CP 300 h 210 h 1 Every 2. Sem.

Language Module ownerGerman Prof. Dr. rer. nat. Oskar von Stryk

1 ContentThis course covers spatial representations and transformations, manipulator kinematics, vehicle kinemat-ics, velocity kinematics, Jacobian matrix, robot dynamcis, robot sensors and actuators, robot control, pathplanning, localization and navigation of mobile robots, robot autonomy and robot development.Theoretical and practical assignments as well as programming tasks serve for deepening of the understand-ing of the course topics.

2 Learning objectives / Learning OutcomesAfter successful participation, students possess the basic technical knowledge and methodological skills nec-essary for fundamental investigations and engineering developments in robotics in the fields of modeling,kinematics, dynamics, control, path planning, navigation, perception and autonomy of robots.

3 Recommended prerequisite for participationRecommended: basic mathematical knowledge and skills in linear algebra, multi-variable analysis andfundamentals of ordinary differential equations

4 Form of examinationModule Eccompanying Examination:

• [20-00-0735-iv] (Technical Examination, Written/Oral Examination, Standard BWS)

5 GradingModule Eccompanying Examination:

• [20-00-0735-iv] (Technical Examination, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.

7 Grade bonus compliant to §25 (2)In dieser Vorlesung findet eine Anrechnung von vorlesungsbegleitenden Leistungen statt, die lt. §25 (2)der 5. Novelle der APB und den vom FB 20 am 30.3.2017 beschlossenen Anrechnungsregeln zu einerNotenverbesserung um bis zu 1.0 führen kann.

14

8 References

Courses

Course Nr. Course name20-00-0735-iv Foundations of Robotics

Instructor Type SWSProf. Dr. rer. nat. Oskar von Stryk Integrated

Course6

2.1 Optionals ETiT, MPE, CS 15

Module nameFoundations of Robotics for Mechatronics


Language Module ownerGerman Prof. Dr. rer. nat. Michael Waidner

1 ContentThis course covers spatial representations and transformations, manipulator kinematics, vehicle kinemat-ics, velocity kinematics, Jacobian matrix, robot dynamcis, robot sensors and actuators, robot control, andpath planning.Theoretical and practical assignments as well as programming tasks serve for deepening of the understand-ing of the course topics.

2 Learning objectives / Learning OutcomesAfter successful participation, students possess the basic technical knowledge and methodological skills inthe field of modeling, kinematics, dynamics, control and path planning of robots that are necessary forfundamental investigations and engineering developments in robotics.

3 Recommended prerequisite for participationRecommended: basic mathematical knowledge and skills in linear algebra, multi-variable analysis andfundamentals of ordinary differential equations





6 Usability of this moduleM.Sc. Mechatronics


8 References

Courses

Course Nr. Course name20-00-1109-iv Foundations of Robotics for Mechatronics

Instructor Type SWSProf. Dr. rer. nat. Michael Waidner Integrated

Course5


2.1.2 Additionals Robotics

2.1.2.1 A: Sensors and Actors

Module nameActuator Materials and Principles

Module Nr. Credit Points Workload Self study Duration Cycle offered16-26-5140 4 CP 120 h 90 h 1 SoSe

Language Module ownerGerman Prof. Dr.-Ing. Thilo Bein

1 ContentDefinitions; multifunctional materials; piezoceramics, shape memory alloy, polymer-based transducer ma-terials; actuator principles; sensors; applications.


• Assess the relevance of transducer materials for active and adaptive systems.• Explain the underlying physical principles and properties of transducer materials.• Evaluate the appropriate implementation of transducer materials in active and adaptive systems.• Explain the fundamentals of actuator and sensor principles.• Apply transducer materials in the design of actuators and sensors.



• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Oral exam 30 min


• Module Examination (Technical Examination, Technical Examination, Weighting: 100 %)

6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik (Vertiefung Adaptronik)


8 ReferencesCopies of overhead transparencies. Extract from “Grundwissen des Ingenieurs”, Chapter 22. Both will bedistributed in the lecture.Hering, E.; Modler, H. (ed.): Grundwissen des Ingenieurs, Hansa Verlag, Leipzig, 2002.Gasch, R.; Knothe, K.: Strukturdynamik, Band 1 & 2, Springer-Verlag, Berlin, 1987 und 1989.Heimann, B.; Gerth, W.; Popp, P.: Mechatronik, Fachbuchverlag, Leipzig, 1998.Ruschmeyer, K.; u. a.: Piezokeramik, Expert Verlag, Rennigen-Malmsheim, 1995.Duerig, T. W.: Engineering Aspects of Shape Memory Alloys, London, Butterworth-Heinemann, 1990.Janocha, H.: Actuators: Basics and Applications, 1. Auflage, Springer Verlag, Berlin, 2004.

Courses

Course Nr. Course name16-26-5140-vl Actuator Materials and Principles



Module nameImage Processing


Language Module ownerGerman Prof. Dr. Bernt Schiele

1 ContentFundamentals of image processing:- Image properties- Image transformations- Simple and complex filtering- Image compression,- Segmentation- Classification

2 Learning objectives / Learning OutcomesAfter successfully completing the course, students have an overview over the mechanisms used in and theabilities of modern image processing techniques. They are able to solve basic to medium level problems inimage processing.






6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikMay be used in other degree programs.


8 References- Gonzalez, R.C., Woods, R.E., “”Digital Image Processing"", Addison- Wesley Publishing Company, 1992- Haberaecker, P., ""Praxis der Digitalen Bildverarbeitung und Mustererkennung"", Carl Hanser Verlag, 1995- Jaehne, B., ""Digitale Bildverarbeitung"", Springer Verlag, 1997

Courses

Course Nr. Course name20-00-0155-iv Image Processing

Instructor Type SWSIntegratedCourse

2


Module nameComputer Vision in Engineering



1 ContentA Basics

• Scene Representation2D and 3D Geomtery• Image Acquisition

– Geometric Projections Camera Calibration

• Objective and Illumination• Discrete 2D signals

– Separability, Sampling– Transformation, Interpolation– Convolution, Correlation– Discrete Fourier Transformation

B Basics of Image Analysis• Filtering

– Basics2D Filter Design– Linear Filtering– Nichtlinear Filtering

• Image Decompositions– Multi-scale Representation– Pyramids– Filter Banks

• Image Features– Structure– Moments, Histograms

2 Learning objectives / Learning OutcomesThe lecture communicates mathematical basics needed to solve computer vision problems in the field ofengineering. The focus is on methods that are relevant for measuring and control tasks. Applications rangefrom visual quality inspection, visual robotics, photogrammetry, visual odometry up to visually guideddriver assistance etc.The students should obtain a good understanding for the relations between the three-dimensional worldand its two-dimensional projection onto the image plane of a camera. They also should learn about meth-ods that exist to infer knowledge from the world given image data. They should develop some feeling forthe different kinds of problems that arise in computer vision and how to choose an efficient solution interms of algorithms.




5 Grading


Module Final Examination:• Module Examination (Technical Examination, Optional, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc iST, MSc CE, MSc iST


8 ReferencesReferences / Textbooks: Lecture slides, exercise sheets and matlab-code.Further reading

• Yi Ma, Stefano Soatto, Jana Kosecka und Shankar S. Sastry, An Invitation to 3-D Vision - From Imagesto Geometric Models, Springer, 2003.

• Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Second Edi-tion, Cambridge University Press, 2004.

• Karl Kraus, Photogrammetrie, Band 1 Geometrische Informationen aus Photographien und Laser-scanneraufnahmen 7. Auflage, de Gruyter Lehrbuch, 2004.

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2006.• Bernd Jähne, Digital Image Processing, 6. Auflage, 2005.

Courses

Course Nr. Course name18-ad-2090-vl Computer Vision in Engineering

Instructor Type SWSDr.-Ing. Thomas Guthier, Prof. Dr.-Ing. Jürgen Adamy Lecture 2

Course Nr. Course name18-ad-2090-ue Computer Vision in Engineering

Instructor Type SWSDr.-Ing. Thomas Guthier Practice 1


Module nameComputer Vision I


Language Module ownerEnglish Prof. Dr. Bernt Schiele

1 Content- Basics of image formation- Linear and (simple) nonlinear image filtering- Foundations of multi-view geometry- Camera calibration and pose estimation- Foundations of 3D reconstruction- Foundations of motion estimation from video- Template and subspace methods for object recognition- Object classification with bag of words- Object detection- Basics of image segmentation

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students are familiar with the basics of computer vision. Theyunderstand fundamental techniques for the analysis of images and videos, can name their assumptions andmathematical formulations, as well as describe the resulting algorithms. They are able to implement thesetechniques in order to solve basic image analysis tasks on realistic imagery.

3 Recommended prerequisite for participationParticiation of lecture Visual Computing is recommended.







8 ReferencesLiterature recommendations will be updated regularly, an example might be:- R. Szeliski, “”Computer Vision: Algorithms and Applications"", Springer 2011- D. Forsyth, J. Ponce, ""Computer Vision – A Modern Approach"", Prentice Hall, 2002

Courses


Course Nr. Course name20-00-0157-iv Computer Vision


4


Module nameComputer Vision II


Language Module ownerEnglish Prof. Dr. Bernt Schiele

1 Content- Computer vision as (probabilistic) inference- Robust estimation and modeling- Foundations of Bayesian networks and Markov random fields- Basic inference and learning methods in computer vision- Image restoration- Stereo- Optical flow- Bayesian tracking of (articulated) objects- Semantic segmentation- Current research topics

2 Learning objectives / Learning OutcomesAfter successfully attending the course, students have developed a more in-depth understanding of com-puter vision. They formulate image and video analysis tasks as inference problems, taking challenges ofreal applications into account, e.g. regarding robustness. They solve the inference problem using discreteor continuous inference algorithms, and apply these to realistic imagery. They quantitatively evaluate theapplication specific results.

3 Recommended prerequisite for participationParticipation of lecture Visual Computing and Computer Vision I is recommended.





6 Usability of this moduleB.Sc. InformatikM.Sc. InformatikB.Sc. Computational EngineeringM.Sc. Computational EngineeringM.Sc. WirtschaftsinformatikB.Sc. Psychologie in ITJoint B.A. InformatikB.Sc. Sportwissenschaft und InformatikM.Sc. Sportwissenschaft und InformatikCan be used in other degree programs.


8 ReferencesLiterature recommendations will be updated regularly, an example might be:- S. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012- R. Szeliski, “”Computer Vision: Algorithms and Applications"", Springer 2011

Courses


Course Nr. Course name20-00-0401-iv Computer Vision II


4


Module nameDigital Signal Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-2060 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Abdelhak Zoubir

1 Content1) Discrete-Time Signals and Linear Systems – Sampling and Reconstruction of Analog Signals2) Digital Filter Design – Filter Design Principles; Linear Phase Filters; Finite Impulse Response Filters;Infinite Impulse Response Filters; Implementations3) Digital Spectral Analysis - Random Signals; Nonparametric Methods for Spectrum Estimation; Paramet-ric Spectrum Estimation; Applications;4) Kalman Filter

2 Learning objectives / Learning OutcomesStudents will understand basic concepts of signal processing and analysis in time and frequency of deter-ministic and stochastic signals. They will have first experience with the standard software tool MATLAB.

3 Recommended prerequisite for participationDeterministic signals and systems theory





6 Usability of this moduleBSc ETiT, Wi-ETiT, MSc Medizintechnik


8 ReferencesCourse manuscriptAdditional References:

• A. Oppenheim, W. Schafer: Discrete-time Signal Processing, 2nd ed.• J.F. Böhme: Stochastische Signale, Teubner Studienbücher, 1998

Courses

Course Nr. Course name18-zo-2060-vl Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir, M.Sc. Martin Gölz Lecture 3

Course Nr. Course name18-zo-2060-ue Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir, M.Sc. Martin Gölz Practice 1


Module nameFundamentals of Adaptronics


Language Module ownerGerman Prof. Dr.-Ing. Holger Hanselka

1 ContentDefinitions of smart passive, adaptive, and active systems; multifunctional materials; piezoceramics, shapememory materials, electro- and magnetorheological fluids, dielectric polymers; actuators; smart dampers,adaptive absorbers, inertial mass actuators, active mounts; design process and principles; methods forvibration control; feedback control; electromechanical analogy, shunt damping; applications.


• Analyze mechatronic and smart, i.e., adaptronic structural systems.• Explain major vibration control principles, their mode of operation, and the enhanced potentials of

smart systems such as piezoceramics, shape memory alloys, or smart fluids as well as evaluate smartvibration control solutions.

• Analyse physical principles, characteristics, and limitations of smart materials and evaluate and selectsuitable mechanisms for certain boundary conditions.

• Explain smart actuators for vibration control and select suitable mechanisms for certain boundaryconditions.

• Evaluate application possibilities of smart structural solutions and their limitations.

3 Recommended prerequisite for participationvibration technology





6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik


8 Referencescopies of transperanciesFuller, C., Elliot, S., Nelson, P.: Active Control of Vibration. London: Academic Press 1996Hansen, C.H. , Snyder, S.D.: Active Control of Noise and Vibration, London: E&FN Spon 1997Ruschmeyer, K., u.a.: Piezokeramik. Rennigen-Malmsheim: expert verlag 1995Utku, S.: Theory of Adaptive Structures, Boca Raton: CRC Press LLC 1998Duerig, T.W.: Engineering Aspects of Shape Memory Alloys, London, Butterworth-Heinemann, 1990

Courses

Course Nr. Course name16-26-5030-vl Fundamentals of Adaptronics



Module nameSensor Signal Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kn-2130 3 CP 90 h 60 h 1 SoSe


1 ContentThe module provides knowledge in-depth about the measuring and processing of sensor signals. In thearea of primary electronics, some particular characteristics such as errors, noise and intrinsic compensationof bridges and amplifier circuits (carrier frequency amplifiers, chopper amplifiers, Low-drift amplifiers)in terms of error and energy aspects are discussed. Within the scope of the secondary electronic, theclassical and optimal filter circuits, modern AD conversion principles and the issues of redundancy anderror compensation will be discussed.

2 Learning objectives / Learning OutcomesThe Students acquire advanced knowledge on the structure of modern sensors and sensor proximity signalprocessing. They are able to select appropriate basic structure of modern primary and secondary electronicsand to consider the error characteristics and other application requirements.

3 Recommended prerequisite for participationMeasuring Technique, Sensor Technique, Electronic, Digital Signal Processing





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc MEC


8 References• Slide set of lecture• Skript of lecture• Textbook Tränkler „Sensortechnik“, Springer• Textbook Tietze/Schenk „Halbleiterschaltungstechnik“, Springer

Courses

Course Nr. Course name18-kn-2130-vl Sensor Signal Processing

Instructor Type SWSProf. Dr. Mario Kupnik Lecture 2


Module nameSensor Technique



1 Content

2 Learning objectives / Learning OutcomesThe Students acquire knowledge of the different measuring methods and their advantages and disadvan-tages. They can understand error in data sheets and descriptions interpret in relation to the applicationand are thus able to select a suitable sensor for applications in electronics and information, as well processtechnology and to apply them correctly.

3 Recommended prerequisite for participationMeasuring Technique





6 Usability of this moduleMSc ETiT, MSc WI-ETiT, MSc MEC, MSc Medizintechnik


8 References• Slide set of lecture• Script of lecture• Textbook Tränkler „Sensortechnik“, Springer• Exercise script

Courses

Course Nr. Course name18-kn-2120-vl Sensor Technique

Instructor Type SWSProf. Dr. Mario Kupnik Lecture 2

Course Nr. Course name18-kn-2120-ue Sensor Technique

Instructor Type SWSProf. Dr. Mario Kupnik Practice 1


2.1.2.2 B: Systems and Artificial Intelligence

Module nameIntroduction to Artificial Intelligence


Language Module ownerGerman Prof. Dr. techn. Johannes Fürnkranz

1 ContentArtificial Intelligence (AI) is concerned with algorithms for solving problems, whose solution is generallyassumed to require intelligence. While research in the early days was oriented on results about humanthinking, the field has since developed towards solutions that try to exploit the strengths of the computer.In the course of this lecture we will give a brief survey over key topics of this core discipline of computerscience, with a particular focus on the topics search, planning, learning, and reasoning. Historical andphilosophical foundations will also be considered.- Foundations- Introduction, History of AI (RN chapter 1)- Intelligent Agents (RN chapter 2)- Search- Uninformed Search (RN chapters 3.1 - 3.4)- Heuristic Search (RN chapters 3.5, 3.6)- Local Search (RN chapter 4)- Constraint Satisfaction Problems (RN chapter 6)- Games: Adversarial Search (RN chapter 5)- Planning- Planning in State Space (RN chapter 10)- Planning in Plan Space (RN chapter 11)- Decisions under Uncertainty- Uncertainty and Probabilities (RN chapter 13)- Bayesian Networks (RN chapter 14)- Decision Making (RN chapter 16)- Machine Learning- Neural Networks (RN chapters 18.1,18.2,18.7)- Reinforcement Learning (RN chapter 21)- Philosophical Foundations

2 Learning objectives / Learning OutcomesAfter a successful completion of this course, students are in a position to- understand and explain fundemental techniques of artificial intelligence- participate in a discussion about the possibility of an artificial intelligence with well-founded arguments- critically judge new developments in this area






6 Usability of this module


B.Sc. InformatikM.Sc. InformatikMay be used in other degree programs.


8 References

Courses

Course Nr. Course name20-00-1058-iv Intruduction to Artificial Intelligence

Instructor Type SWSProf. Dr. techn. Johannes Fürnkranz Integrated

Course3


Module nameElectromechanical Systems I



1 ContentStructure and design methods of elektromechanical systems, mechanical, acoustical and thermal networks,transducers between mechanical and acoustical networks. Design and devices of electromechanical trans-ducers.

2 Learning objectives / Learning OutcomesComprehension, description, calculation and application of the most relevant electromechanical transduc-ers, comprising electrostatic transducer (e.g. microphone and accelerometer), piezoelectric transducers(e.g micro motors, micro sensors), electrodynamic transducer (loudspeaker, shaker), piezomagnetic trans-ducer (e.g. ultrasonic source). Design of complex electromechanical systems like sensors and actuatorsand their applications by applying the discrete element network method.

3 Recommended prerequisite for participationElectrical Engineering and Information Technology I





6 Usability of this moduleBSc ETiT, BSc WI-ETiT, MSc MEC


8 ReferencesBook: Electromechanical Systems in Microtechnic und Mechatronic, Springer 2012, Script for lecture Elec-tromechanical Systems I, Workbook

Courses

Course Nr. Course name18-kn-1050-vl Electromechanical Systems I


Lecture 2

Course Nr. Course name18-kn-1050-ue Electromechanical Systems I


Practice 2


Module nameDesign of Human-Machine-Interfaces


Language Module ownerGerman Prof. Dr.-Ing. Ralph Bruder

1 ContentCase studies of human-machine-interfaces, basics of system theory, user modelling, human-machine-interaction, interface-design, usability.


• Reflect the technical development of human-machine interfaces using examples• Describe human-machine interfaces in system theoretical terminology• Explain models of human information processing and the related application issues• Apply the human-centered product development process in accordance with DIN EN ISO 9241-210• Analyse the use context of products for the deduction of user requirements• Implement the design criterias using the guidelines for the design of human-machine systems• Assess the usability of products using methods with and without user involvement






6 Usability of this moduleWP Bachelor MPEBachelor Mechatronik


8 ReferencesLecture notes available on the internet (www.arbeitswissenschaft.de)

Courses

Course Nr. Course name16-21-5040-vl Design of Human-Machine-Interfaces


Course Nr. Course name16-21-5040-ue Design of Human-Machine-Interfaces



Module nameMechatronic Systems Engineering I


Language Module ownerGerman Prof. Dr.-Ing. Stephan Rinderknecht

1 ContentStructural dynamics for mechatronic systems; control strategies for mechatronic systems; components formechatronic systems: actuators, amplifier, controllers, microprocessors, sensors.


• Model the structural dynamic components.• Design the best suited controllers for rigid and elastic system components.• Simulate complete mechatronic systems (control loops) under simplified considerations for actuators

and sensors.• Explain the static and dynamic behaviour of the mechatronic system.



• Module Examination (Technical Examination, Oral Examination, Duration: 20 min, Standard Grad-ing System)

Oral exam 20 min


• Module Examination (Technical Examination, Oral Examination, Weighting: 100 %)

6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)


8 Referenceslectures notes

Courses

Course Nr. Course name16-24-5020-vl Mechatronic Systems in Mechanical Engineering I


Course Nr. Course name16-24-5020-ue Mechatronic Systems in Mechanical Engineering I



Module nameMachine Learning and Deep Learning for Automation Systems



1 Content• Concepts of machine learning• Linear methods• Support vector machines• Trees and ensembles• Training and assessment• Unsupervised learning• Neural networks and deep learning• Convolutional neuronal networks (CNNs)• CNN applications• Recurrent neural networks (RNNs)

2 Learning objectives / Learning OutcomesStudents will get a broad and practical view on the field of machine learning. First, the most relevantalgorithm classes of supervised and unsupervised learning are discussed. After that, the course addressesdeep neural networks, which enable many of today’s applications in image and signal processing. Thefundamental characteristics of all algorithms are compiled and demonstrated by programming examples.Students will be able to assess the methods and apply them to practical tasks.

3 Recommended prerequisite for participationFundamental knowledge in linear algebra and statisticsPreferred: Lecture “Fuzzy logic, neural networks and evolutionary algorithms”


• Module Examination (Technical Examination, Written/Oral Examination, Duration: 90 min, Stan-dard Grading System)

The examination takes place in form of a written exam (duration: 90 minutes). If one can estimate thatless than 7 students register, the examination will be an oral examination (duration: 30 min.). The type ofexamination will be announced in the beginning of the lecture.


• Module Examination (Technical Examination, Written/Oral Examination, Weighting: 100 %)



8 References• T. Hastie et al.: The Elements of Statistical Learning. 2. Aufl., Springer, 2008• I. Goodfellow et al.: Deep Learning. MIT Press, 2016• A. Géron: Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. 2. Aufl., O’Reilly,

2019

Courses


Course Nr. Course name18-ad-2100-vl Machine Learning and Deep Learning for Automation Systems

Instructor Type SWSDr.-Ing. Michael Vogt Lecture 2


Module nameMachine Learning for Robotics & Mechatronics



1 ContentFoundations from robotics and machine learning for robot learning- Learning of forward models- Representation of a policy, hierarchical abstraction wiith movement primitives- Imitation learning- Optimal control with learned forward models- Reinforcement learning and policy search- Inverse reinforcement learning

2 Learning objectives / Learning OutcomesAfter students have completed this course, they can reproduce the foundations of the field of MachineLearning for Robotics and independently conduct research projects in the field of Machine Learning forRobotics & Mechatronics, e.g. as part of a bachelor or master thesis. The resulting fundamental under-standing allows graduates of the course to understand the algorithmic approaches to machine learningand to apply them practically in robotics and mechatronics. This knowledge enables them to synthesizepractically new approaches.

3 Recommended prerequisite for participationRecommended: Good programming in Python.Lecture Foundations of Robotics for Mechatronics is helpful but not mandatory.


• [20-00-1113-vl] (Technical Examination, Written/Oral Examination, Standard BWS)


• [20-00-1113-vl] (Technical Examination, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleM.Sc. Mechatronic


8 References

Courses

Course Nr. Course name20-00-1113-vl Machine Learning for Robotics & Mechatronics

Instructor Type SWSProf. Dr. rer. nat. Michael Waidner Lecture 4


Module nameMachine Learning Applications


Language Module ownerGerman Prof. Dr.-Ing. Uwe Klingauf

1 ContentTheory: Application-oriented basics of machine learning and related areas statistics (descriptive, explo-rative, inductive), advanced analytics, data mining, data science and big data; basics of machine learningmethods, functions and algorithms; development processes; basics of data science principles and tech-niques: discussion of business scenarios; collection, review and quality evaluation of data; data prepara-tion, feature engineering; application of methods and use of program systems on the basis of examples;identification and evaluation of possible solutions; model selection, optimization, performance-assessment;essential ideas of model integration in decision-making processes, recommendations for actions, system ofsystems; examples from current research, e.g. predictive maintenance in aviation and production;Practical group work: Application of basic features of a software development methodology (e.g. scrum);application of theoretical knowledge on a cooperative development task; practical solution development ofan industrial challenge through programming and data evaluation (implementation); documentation andpresentation of the results;

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1.Assess and evaluate basic developments and possible uses of artificial intelligence (machine learning) inengineering applications (e. g. mechanical engineering)2.Differentiate and explain key concepts and (mathematical) methods of machine learning3.Evaluate selected algorithms and models (e.g. from the diagnostic/prognostic domain) with regard totheir performance, robustness and quality from an engineering point of view4.Apply learned competencies in the areas of data acquisition and processing, data-based modelling (diag-nosis and prognosis) and prescription5.Structure simple and medium analytical tasks independently by means of standardized processes(CRISP/OSA-CBM), realize them with given data and estimate their economic impact (business value)

3 Recommended prerequisite for participationProgramming knowledge in Matlab and/or Python is required.


• Module Examination (Technical Examination, Special Form, Standard Grading System)Special form: 50 % written exam (60 min.) and 50 % practical group work (during semester) of a co-operative development task (“hackathon”) incl. implementation, documentation and presentation (extradate)


• Module Examination (Technical Examination, Special Form, Weighting: 100 %)



8 ReferencesSciptum via Moodle.Ertel: Grundkurs künstliche Intelligenz, SpringerMitchell: Machine Learning, McGraw HillHastie: The Elements of Statistical Learning, SpringerWitten: Data Mining, Elsevier


Courses

Course Nr. Course name16-98-4174-vl Machine Learning Applications


Course Nr. Course name16-98-4174-pr Machine Learning Applications (Group Work)

Instructor Type SWSInternship 0


Module nameMechatronic Systems Engineering II



1 ContentActuators; Human-Machine-Interface; development methods, system Integration.


• Explain the functional principles of electromagnetic, electrodynamic, and piezoelectric actuators andreasonably apply these.

• Explain the general principles of human-machine-interfaces on the basis of examples.• Describe methods and requirements for the development of complex mechatronic systems.• Apply mechatronic system thinking for the purpose of system integration and optimization of

different examples.

3 Recommended prerequisite for participationBasic knowledge of mechatronics, engineering mechanics, electrical engineering and control engineeringis required.



Oral exam 30 min



6 Usability of this moduleWPB Master MPE II (Kernlehrveranstaltungen aus dem Maschinenbau)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)


8 ReferencesLecture handouts can be downloaded in the intranet.Nordmann, R.; Birkhofer, H.: Maschinenelemente und Mechatronik I.Schröder, D.: Elektrische Antriebe - Grundlagen.Bertsche, B.; Naunheimer, H.; Lechner, G.: Fahrzeuggetriebe.Löw, P.; Pabst, R.; Petry, E.: Funktionale Sicherheit in der Praxis.

Courses

Course Nr. Course name16-24-5030-vl Mechatronic Systems in Mechanical Engineering II


Course Nr. Course name16-24-5030-ue Mechatronic Systems in Mechanical Engineering II



Module nameHuman-mechatronics systems


Language Module ownerEnglish Dr.-Ing. Philipp Beckerle

1 ContentThis course intends to convey both technical and human-oriented aspects of mechatronic systems thatwork close to humans. The technology-oriented part of the lecture focuses on the modeling, design, andcontrol of elastic and wearable mechatronic and robotic systems. Research-related topics such as thedesign of energy-efficient actuators and controllers, body-attached measurement technology, and fault-tolerant human-machine/robot interaction will be included. The focus of the human-oriented part is onthe analysis of users’ demands and the consideration of such human factors in component and systemdevelopment.To deepen the contents, flip-the-classroom sessions will be conducted in which relevant research resultswill be presented and discussed by the students.Human-mechatronics systems; wearable robotic systems; human-oriented design methods; biomechanics;biomechanical models; elastic robots; elastic drives; elastic robot control; human-robot interaction; systemintegration; fault handling; empirical research methods.

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1.Tackle challenges in human-mechatronic systems design interdisciplinary2.Use engineering methods for modeling, design, and control in human-mechatronic systems development.3. Apply methods from psychology (perception, experience), biomechanics (motion and human models),and engineering (design methodology) and interpret their results.4.Develop mechatronic and robotic systems that are provide user-oriented interaction characteristics inaddition to efficient and reliable operation.








8 References- Ott, C. (2008). Cartesian impedance control of redundant and flexible-joint robots. Springer.- Whittle, M. W. (2014). Gait analysis: an introduction. Butterworth-Heinemann.- Burdet, E., Franklin, D. W., & Milner, T. E. (2013). Human robotics: neuromechanics and motor control.MIT press.- Gravetter, F. J., & Forzano, L. A. B. (2018). Research methods for the behavioral sciences. CengageLearning.

Courses


Course Nr. Course name16-24-3134-vl Human-mechatronics systems



Module nameRobotics in industry: Basics and application


Language Module ownerGerman Dr. rer. nat. Debora Clever

1 ContentIntroduction to robotics: kinematics, dynamic, control; industrial robots; robot safety; human-robot col-laboration; from automation to autonomization (optimization and machine learning); industry insights;digression into patent law (guest lecture); Exercise partly as a block event, every 2 - 4 weeks.

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1. Evaluate the added value of industrial robots within production lines and along the entire value chain.2. Derive equations of motion for manipulators and use these equations in the area of motion planningand control.3. Know different safety concepts with focus on human robot cooperation and select / adapt them accord-ing to the situation.4. Recognize the optimization and learning potential of concrete robotic applications and select / applycorresponding algorithms.5. Be able to describe the procedure for protecting your own inventions.








8 ReferencesHandouts for the lecture will be made available for download after each lecture (moodle).

Courses

Course Nr. Course name16-24-3124-vl Robotics in industry: Basics and application


Course Nr. Course name16-24-3124-ue Robotics in industry: Basics and application



Module nameOptimal and Predictive Control

Module Nr. Credit Points Workload Self study Duration Cycle offered18-fi-2010 4 CP 120 h 75 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Rolf Findeisen

1 ContentOptimal control approaches, like model predictive control, are one of the most versatile, flexible and mostoften used modern control approaches by now. Fields of applications span from robotics, autonomous driv-ing, aerospace systems, energy systems, chemical processes, biotechnology, up to biomedicine. The lectureprovides an introduction to fundamentals of optimal control, focusing on the method and theoretical base.It furthermore provides an outreach towards efficient numerical solution strategies and model predictivecontrol.The following topics are covered during the lecture:

• Application examples from various fields such mechatronics, robotics, electrical systems, chemicalprocesses, economics, as well as aeronautics

• Review of nonlinear programming• Dynamic programming, the principle of optimality, Hamilton-Jacobi-Bellman equation• Pontryagin maximum principle• Infinite and finite-horizon optimal control, LQ optimal control• Numerical solution approaches for optimal control problems• Introduction to model predictive control (MPC)

2 Learning objectives / Learning OutcomesThe students learn how to formulate, analyze, and solve optimal control problems. The course focuseson key ideas and concepts of optimal control. The students learn standard methods for computing andimplementing optimal control strategies.

3 Recommended prerequisite for participationBasic lecture of control engineering and system theory with a focus on state space formulations





6 Usability of this moduleMSc etitMSc MECMSc Wi-etitOpen for other departments and Study Programmes


8 References


Lecture notes and slides will be provided in the elearning systemFurther recommended literature:Optimal Control

• R. Bellman. Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1957.• L.D. Berkovitz. Optimal Control Theory. Springer-Verlag, New York, 1974.• D.P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific Press. 2nd edition,

2000.• L.M. Hocking. Optimal Control. An Introduction to the Theory with Applications. Oxford Applied

Mathematics and Computing Science Series. Oxford University Press, Oxford, 1991.• J.L. Troutmann. Variational Calculus and Optimal Control. Undergraduate Texts in Mathematics.

Springer, 1991.

Optimization• S. Boyd, L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.• J. Nocedal, S. Wright. Numerical Optimization. Springer, 2006.

Model Predictive Control• J.B. Rawlings, D.Q. Mayne, M. Diehl. Model Predictive Control: Theory and Design, 2009.

Courses

Course Nr. Course name18-fi-2010-vl Optimal and Predictive Control

Instructor Type SWSProf. Dr.-Ing. Rolf Findeisen Lecture 2

Course Nr. Course name18-fi-2010-ue Optimal and Predictive Control

Instructor Type SWSProf. Dr.-Ing. Rolf Findeisen Practice 1


Module nameAutomated Driving


Language Module ownerEnglish Prof. Dr.-Ing. Jürgen Adamy

1 Content• History of Automated Driving• Terminology and Paths towards Automated Driving• Architectures, Building Blocks, and Components• Perception & Environment Models• Data Fusion & State Estimation

– Deep Dive: Target Tracking & Traffic Participant Fusion– Deep Dive: Grid Fusion & Free Space Estimation– Deep Dive: Road Model Fusion

• Localization, Digital Maps, and Vehicle-To-X Communication• Situation Understanding, Prediction, and Criticality Assessment

– Deep Dive: Probabilistic Driving Maneuver Detection

• Behavior & Trajectory Planning, Decision Making• Automated Driving Software Development & Test• Open Challenges & State-of-the-Art Research Topics

2 Learning objectives / Learning OutcomesAfter visiting the lecture, the student

• is familiar with the history and terminology of automated driving systems,• knows important architectures, building blocks, and components of automated vehicles,• understands different perception, environment model, and data fusion approaches,• has an idea about relevant methods (e.g. Bayesian Inference & Probabilistic Graphical Models, State

Estimation, Deep Learning, Dempster-Shafer Theory) and knows how they can be beneficially ap-plied in different of automated driving areas (e.g. detection, target tracking & traffic participantfusion, grid fusion, road model fusion, localization),

• is familiar with the challenges of situation understanding, prediction, and criticality assessment andknows exemplary methods to tackle the problem,

• is aware of exemplary behavior & trajectory planning approaches,• knows best practices about automated driving software development & test (e.g. continuous inte-

gration, verification & validation, test-driven development, key performance indicators), and• is familiar with open challenges and research topics.






6 Usability of this moduleMsc etit, Msc MEC, Msc Wi-etit, Msc ICE, Msc CE, Msc Informatik



8 ReferencesOwn lecture slides are distributed in advance of any lecture. For more detailed insights into the topic area,the following books can be recommended:

• Eskandarian, A.: Handbook of Intelligent Vehicles. Springer, London, 2012.• Siciliano, B.; Khatib, O.: Springer Handbook of Robotics. 2nd Edition, Springer, Berlin Heidelberg

2016.• Thrun, S.; Burgard, W.; Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents.

The MIT Press, Cambridge, 2006.• Watzenig, D.; Horn, M.: Automated Driving. Safer and More Efficient Future Driving. Springer,

Switzerland, 2017.• Winner, H. et al.: Handbook of Driver Assistance Systems. Basic Information, Components and

Systems for Active Safety and Comfort. Springer, Switzerland, 2016.

Courses

Course Nr. Course name18-ad-2110-vl Automated Driving



2.1.2.3 C: Methodological competences

Module nameManufacturing Automation


Language Module ownerGerman Prof. Dr.-Ing. Matthias Weigold

1 ContentThe lecture gives an overview of the approaches to automation in manufacturing. First of all, the prerequi-sites for automation, but also the general economic conditions are presented. The essential contents are:Introduction to automation technology; Industry 4.0 in the context of connectivity and data aquisition;modern industrial communication within OT and IT (OT=Operational Technology und IT); Current de-velpopment and innovation at Industrial Internet of Things (IIoT); bus systems communication and proto-colls; Sensor technology in automation; Handling and Assembly in industrial production; Industrial robotsand their areas of application in automation technology.

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1. Describe the possibilities and methods concerning manufacturing automation.2. Identify the principles of handling workpieces (sorting, feeding, assembling) as well as the compositionof industrial robots and flexible assembling systems for the manufacturing automation.3. Optimize the degree of automation.4. Give hints concerning a suitable assembly design to the product developer.5. Calculate the economic efficiency of alternative manufacturing systems with different level of automa-tion.








8 ReferencesLecture notes are available during the course and in PTW’s secretariat

Courses

Course Nr. Course name16-09-5030-vl Manufacturing Automation



Module nameDigital Printing


Language Module ownerGerman Prof. Dr. Edgar Dörsam

1 ContentTerminology of digital printing; Workflow, screening, raster technology; Tonal value; Technology of digitalprinting (electrophotography, inkjet, thermal transfer printing); Toner, ink and print substrate; Design.


• Explain terms and the classification system of digital printing technology.• Estimate the fields of application (of digital printing technologies).• Describe the different principles of workflows.• Describe the meaning of the term screening and the reproduction of halftones.• Precisely explain the principles and technical details of electrophotography, thermal transfer printing,

and inkjet printing.• Give a general overview of different construction principles of digital printing systems.• Rate environmental properties of digital printing systems.






6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master ETiT INMT


8 ReferencesThe current lecture notes can be downloaded from the web pages of the institute while the semester is insession.

Courses

Course Nr. Course name16-17-5030-vl Digital Printing



Module nameDigital Signal Processing

Module Nr. Credit Points Workload Self study Duration Cycle offered18-zo-2060 6 CP 180 h 120 h 1 WiSe

Language Module ownerEnglish Prof. Dr.-Ing. Abdelhak Zoubir

1 Content1) Discrete-Time Signals and Linear Systems – Sampling and Reconstruction of Analog Signals2) Digital Filter Design – Filter Design Principles; Linear Phase Filters; Finite Impulse Response Filters;Infinite Impulse Response Filters; Implementations3) Digital Spectral Analysis - Random Signals; Nonparametric Methods for Spectrum Estimation; Paramet-ric Spectrum Estimation; Applications;4) Kalman Filter

2 Learning objectives / Learning OutcomesStudents will understand basic concepts of signal processing and analysis in time and frequency of deter-ministic and stochastic signals. They will have first experience with the standard software tool MATLAB.

3 Recommended prerequisite for participationDeterministic signals and systems theory





6 Usability of this moduleBSc ETiT, Wi-ETiT, MSc Medizintechnik


8 ReferencesCourse manuscriptAdditional References:

• A. Oppenheim, W. Schafer: Discrete-time Signal Processing, 2nd ed.• J.F. Böhme: Stochastische Signale, Teubner Studienbücher, 1998

Courses

Course Nr. Course name18-zo-2060-vl Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir, M.Sc. Martin Gölz Lecture 3

Course Nr. Course name18-zo-2060-ue Digital Signal Processing

Instructor Type SWSProf. Dr.-Ing. Abdelhak Zoubir, M.Sc. Martin Gölz Practice 1


Module nameReal-Time Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-2020 6 CP 180 h 120 h 1 SoSe

Language Module ownerGerman Prof. Dr. rer. nat. Andreas Schürr

1 ContentThe lecture basically covers a model-driven software engineering process which is specially customizedfor real-time systems. This process is more deeply explored in the exercise using an automotive example.A focus is laid on object-oriented techniques. In this context, a real-time specific state-of-the-art CASEtool is introduced and used. Furthermore, fundamental characteristics of real-time systems and systemarchitectures are introduced. Scheduling algorithms are discussed to get insights into real-time operatingsystems. Finally, a comparison between the Java programming language and its expansion for real-timeoperating systems (RT Java) will conclude the lecture.

2 Learning objectives / Learning OutcomesStudents, who have successfully attended this lecture have acquired skills needed for the model-driven andobject-oriented development of embedded real-time systems. This includes a deeper understanding of thefollowing topics:

• classification of real-time systems• create and analyze executable models• application of real-time scheduling algorithms• evaluation and comparison of pros/cons of real-time programming languages as well as real-time

operating systems

3 Recommended prerequisite for participationBasic knowledge of software engineering techniques and excellent knowledge of at least one object-oriented programming language (preferably Java)





6 Usability of this moduleMSc ETiT, BSc iST, MSc Wi-ETiT, BSc Informatik


8 Referenceswww.es.tu-darmstadt.de/lehre/es/

Courses

Course Nr. Course name18-su-2020-vl Real-Time Systems

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Lecture 3

Course Nr. Course name18-su-2020-ue Real-Time Systems

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Practice 1


http://www.es.tu-darmstadt.de/lehre/es/

Module nameMachine Design I


Language Module ownerGerman Dr. Matthias Scheitza

1 Content• Repetition of essential contents of basic courses such as product development, standards, patents,

machine elements, materials science, and cost accounting.• Principles and instructions of the preliminary design and dimensioning of machine elements.• Theory of product and production specific aspects of the definition of surface, form, and position

tolerances.• Presentation of dimensioning methods and documentation requirements.• Exercises to prepare a component from concept to drawing.


• Recognize the interaction between product and manufacturing process design.• Choose in an early stage manufacturing technologies with its corresponding property profiles to

transform a design into a product successfully.• Combine and apply methods of technical and economical design of products.• Define tolerance ranges according to the planned production process.• Select functions oriented material.• Detail the products in an affordable, time-optimized, and functional assembly-friendly design.



• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Oral exam 30-45 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)


8 ReferencesThe lecture notes are available during the lecture. Further literature is referenced in the correspondinglecture.

Courses

Course Nr. Course name16-22-5150-vl Machine Design I



Module nameMachine Design II


Language Module ownerGerman Dr. Matthias Scheitza

1 Content• Introduction of essential machine elements such as bearings from an application point of view• Principles and instructions for the preparation and implementation of a design process in a group of

engineers• Teaching of pre-calculation methods and progress monitoring of design projects• Influences on decisions in the design process and its possible consequences for the product• Exercises to prepare an assembly from design to drawing.


• Calculate preliminary qualitative costs of components and assemblies during the design process.• Identify influential parameters and their effect on the design and manufacturing process and actively

influence them.• Apply progress monitoring methods of design projects.• Analyse design tasks and develop alternative solutions in a team.• Identify and use communication-needs and -possibilities with the company’s instances participating

in the product development.• Apply strategies for the detection and containment of solution fields and make selection decisions in

accordance with the defined requirements or operating conditions.

3 Recommended prerequisite for participationContents of Machine Design I


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Oral exam 30-45 min





8 ReferencesThe lecture notes are available during the lecture. Further literature is referenced in the correspondinglecture.

Courses

Course Nr. Course name16-22-5160-vl Machine Design II



Module nameFuzzy Logic, Neural Networks and Evolutionary Algorithms



1 ContentFuzzy systems: basics, rule based fuzzy logic, design methods, decision making, fuzzy control, patternrecognition, diagnosis; Neural networks: basics, multilayer perceptrons, radial basis functions, patternrecognition, identification, control, interpolation and approximation, Neuro-fuzzy: optimization of fuzzysystems, data driven rule generation; Evolutionary algorithms: optimization problems, evolutionary strate-gies and their applications, genetic programming and its applications


• recalling the elements and set-up of standardized fuzzy-logic, neural networks and evolutionaryalgorithms,

• discussing the pros and cons of certain set- ups of systems from computational intelligence for solvinga given problem,

• recognizing situations in which tools taken from computational intelligence can be applied for prob-lem solving,

• creating programs from algorithms taught in the lecture, and• extending the learned standard procedures in order to solve new problems.






6 Usability of this moduleBSc iST, MSc ETiT, MSc MEC, MSc WI-ETiT, MSc iCE, MSc EPE, MSc CE, MSc Informatik


8 ReferencesAdamy: Fuzzy Logik, Neuronale Netze und Evolutionäre Algorithmen, Shaker Verlag (available for pur-chase at the FG office)www.rtr.tu-darmstadt.de (optionales Material)

Courses

Course Nr. Course name18-ad-2020-vl Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms


Course Nr. Course name18-ad-2020-ue Fuzzy Logic, Neuronal Networks and Evolutionary Algorithms



http://www.rtr.tu-darmstadt.de

Module nameFundamentals of Navigation I


Language Module ownerGerman Prof. Dr.-Ing. Jürgen Beyer

1 ContentNavigation principles, Earth models, Coordinate systems, Radio navigation, Basics and instruments (ADF,VOR, DME, ILS), dead reckoning, functional principles and error analysis, satellite navigation, Introductioninto GPS, signal description and measurement principles, Dilution of Precision (DoP), Differential GPS,Augmentation systems (RAIM, GIC, WAAS, LAAS, EGNOS).


• Explain the physics associated with the navigation of the earth.• Classify common coordinate systems and map projections.• Judge the methods of radio, coupling, and satellite navigation with respect to performance and

applications.

3 Recommended prerequisite for participationRecommanded: Control Engineering



Oral exam (in a group with 3 students) 60 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik


8 ReferencesCourse notes available.

Courses

Course Nr. Course name16-23-5050-vl Fundamentals of Navigation I


Course Nr. Course name16-23-5050-ue Fundamentals of Navigation I



Module nameFundamentals of Navigation II


Language Module ownerGerman Prof. Dr.-Ing. Jürgen Beyer

1 ContentInertial navigation (Structure of strapdown algorithm, error model, Schuler oscillation, barometric aid-ing, ring laser gyro model and functionality). Integrated navigation (Signal blending, Luenberger ob-server, Wiener filter, Kalman filter, failure detection and isolation, open- and closed-loop concept, terraindatabase-based methods). Aircraft navigation (Structure of hybrid navigation, navigation database, navi-gation modes, guidance and control, 4D navigation, required time of arrival), applications, and examples(Map shifts, dead reckoning navigation).


• Judge the methods of inertial and integrated fault tolerant navigation with respect to performanceand applications.

• Describe functions and applications of flight management systems.• Classify current procedures of flight guidance.

3 Recommended prerequisite for participationFundamentals of Navigation I, Control Engineering suggested



Oral exam (in a group with 3 students) 60 min



6 Usability of this moduleWPB Master MPE III (Wahlfächer aus Natur- und Ingenieurwissenschaft)WPB Master PST III (Fächer aus Natur- und Ingenieurwissenschaft für Papiertechnik)Master Mechatronik



Courses

Course Nr. Course name16-23-5060-vl Fundamentals of Navigation II


Course Nr. Course name16-23-5060-ue Fundamentals of Navigation II



Module nameAdvanced Dynamics


Language Module ownerGerman Prof. Dr.-Ing. Bernhard Schweizer

1 ContentIntroduction and definition of multibody systems.Kinematics of rigid bodies; spatial motion (translation and rotation).Formulation of constraint equations (scleronomic, rheonomic, holonomic and nonholonomic constraints);definition of generalized coordinates and virtual displacements.Kinematics of multibody systems; tree-structured systems and systems with closed loops; description ofspatial systems using absolute coordinates and relative coordinates.Kinetics of multibody systems; Newton´s law and Euler´s law; formulation of the equations of motionusing absolute coordinates (Index-3, Index-2 and Index-1 formulations) and relative coordinates.Principle of d´Alembert, principle of virtual power, Lagrange´s equations of the second kind, etc.Linearization of the equations of motion; theory for linear systems with constant coefficients.Applicationexamples: automotive engineering, robotics, gear mechanisms, engine dynamics, rotor dynamics, etc.


• Mathematically describe the spatial motion of a rigid body.• Describe the kinematics of complex planar and spatial dynamical systems.• Derive the equations of motion for complex planar and spatial systems using the Newton-Euler equa-

tions.• Applying the principles of mechanics in order to derive the governing equations of motion (as an

alternative to the Newton-Euler equations).• To generate suitable mathematical models for machines, engines and mechanisms in order to

calculate the motion of the system and the forces/torques acting on the bodies.

3 Recommended prerequisite for participationTechnical Mechanics I to III (Statics, Elastomechanics, Dynamics) and Mathematics I to III recommend.





6 Usability of this moduleMaster MPE PflichtWI/MB, Master Mechatronik


8 ReferencesWoernle, C.: „Mehrkörpersysteme“, Springer, 2011.Shabana, A.: „Dynamics of Multibody Systems”, Cambridge University Press, Third Edition, 2010.Haug, E.J.: „Computer-Aided Kinematics and Dynamics of Mechanical Systems“, Allyn and Bacon, 1989.Markert, R.: „Strukturdynamik“, Shaker, 2013.Dresig, H.; Holzweißig, F.: „Maschinendynamik”, 10. Au-flage, Springer, 2011.

Courses


Course Nr. Course name16-98-4094-vl Machine Dynamics


Course Nr. Course name16-98-4094-hü Advanced Dynamics

Instructor Type SWSLecture HallPractice

2

Course Nr. Course name16-25-5060-gü Advanced Machine Dynamics

Instructor Type SWSGroup Practice 0


Module nameIdentification of Dynamic Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-2040 4 CP 120 h 75 h 1 WiSe


1 Content• Introduction into the determination of mathematical process models based on measured data• Theoretical and experimental modeling of dynamic systems• System identification using continuous time signals:

– Aperiodic signals

* Fourier analysis

* Evaluation of characteristic values (stepresponses)

– Periodic signals

* Frequency response analysis

* Correlation analysis

• System identification using discrete time signals:– Deterministic and stochastic signals– Basics in estimation theory– Correlation analysis

• Parameter estimation techniques:– Least-squares estimation– Model structure determination– Recursive estimation algorithms

• Kalman Filter and Extended Kalman Filter• Numerical Methods• Implementation under MatLab Numerous examples with real experimental data

2 Learning objectives / Learning OutcomesThe students are taught the fundamental methods in signal and system analysis. Furthermore, the studentsmaster methods such as Fourier analysis, correlation analysis and parameter estimation methods. Basedon this foundation, the students are able to assess and to apply the individual methods and can derivenon-parametric as well as parametric models from measured data.

3 Recommended prerequisite for participationMSc ETiT, MSc MEC





6 Usability of this moduleAll disciplines of Electrical Engineering and Information Technology and similar disciplines (Mechatronics,Mechanical and Process Engineering, . . . ), Master of Science


8 References


Pintelon, R.; Schoukens, J.: System Identification: A Frequency Domain Approach. IEEE Press, New York,2001.Ljung, L.: System Identification: Theory for the user. Prentice Hall information and systems sciences series.Prentice Hall PTR, Upper Saddle River NJ, 2. edition, 1999.

Courses

Course Nr. Course name18-ko-2040-vl Identification of Dynamic Systems

Instructor Type SWSDr. Ing. Eric Lenz Lecture 2

Course Nr. Course name18-ko-2040-ue Identification of Dynamic Systems

Instructor Type SWSDr. Ing. Eric Lenz Practice 1


Module nameIndustrial Electronics

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-2210 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman and English Prof. Dr.-Ing. Klaus Hofmann

1 ContentTypical Struture of Industrial Electronics Components. Characteristics of Typical Building Blocks (DigitalCore, Sensor Frontend, Actuator Frontend, Supply and Reference Level), Functioning of Relevant Field BusSystems, Knowledge of Relevant Standards and Technical Regulations.

2 Learning objectives / Learning OutcomesAfter successfull completion of the module, students are able to: 1. understand the use of electroniccomponents in typical industrial environments, 2. understand the function of the building blocks of typicalIE comonents, 3. deeply understand the functioning of analog bulding blocks, 4. understand relevant fieldbus systemes, 5. understand the regulatory and technical standards of industrial electronics components.

3 Recommended prerequisite for participationLecture “Elektronik“ and “Analog IC Design”





6 Usability of this moduleMSc ETiT, M.Sc. iCE, M.Sc. MEC


8 References• Dietmar Schmid, Gregor Häberle, Bernd Schiemann, Werner Philipp, Bernhard Grimm, Günther

Buchholz, Jörg Oestreich, Oliver Gomber, Albrecht Schilling: „Fachkunde Industrieelektronik undInformationstechnik“; Verlag Europa-Lehrmittel, 11 th Ed. 2013.

• Gunter Wellenreuther, Dieter Zastrow; „Automatisieren mit SPS – Theorie und Praxis“; SpringerVerlag, 6 th Ed. 2015.

• Ulrich Tietze, Christoph Schenk, Eberhard Gamm: „Halbleiter-Schaltungstechnik“; Springer Verlag,15 th Ed. 2016.

Courses

Course Nr. Course name18-ho-2210-vl

Instructor Type SWSDr.-Ing. Roland Steck Lecture 2

Course Nr. Course name18-ho-2210-ue

Instructor Type SWSDr.-Ing. Roland Steck Practice 1


Module nameController Design for Multivariable Systems in State Space



1 ContentPole assignment, Coupling and decoupling of linear multivarible systems, Optimal control, Design of stateobservers, Dynamic state feedback control, Structurally constrained state feedback

2 Learning objectives / Learning OutcomesThe students will be able to analyse and design linear time-invariant multivariable systems by means ofdifferent state space design methods.

3 Recommended prerequisite for participationBasic knowledge of linear control theory ("System Dynamics and Control Systems I and II”)







8 ReferencesSkript Konigorski: “Mehrgrößenregler im Zustandsraum”,Anderson, Moore: "Optimal Control: Linear Quadratic Methods", Föllinger:"Regelungstechnik: Einführungin die Methoden und ihre Anwendung", Föllinger: "Optimale Regelung und Steuerung: Eine Einführungfür Ingenieure", Roppenecker: "Zeitbereichsentwurf linearer Regelungen: Grundlegende Strukturen undeine Allgemeine Methodik ihrer Parametrierung",Unbehauen: "Regelungstechnik II:Zustandsregelungen, digitale und nichtlineare Regelungssysteme",Zurmühl: "Matrizen und ihre Anwendung: Für Angewandte Mathematiker, Physiker und Ingenieure. Teil1: Grundlagen"

Courses

Course Nr. Course name18-ko-2050-vl Controller Design for Multivariable Systems in State Space

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski, M.Sc. Viktor Kisner Lecture 2

Course Nr. Course name18-ko-2050-ue Controller Design for Multivariable Systems in State Space

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski, M.Sc. Viktor Kisner Practice 2


Module nameSoftware Engineering - Introduction

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-1010 6 CP 180 h 120 h 1 WiSe


1 ContentThe lecture gives an introduction to the broad discipline of software engineering. All major topics of thefield - as entitled e.g. by the IEEE’s “Guide to the Software Engi-neering Body of Knowledge” - get addressedin the indicated depth. Main emphasis is laid upon requirements elicitation techniques (software analysis)and the design of soft-ware architectures (software design). UML (2.0) is introduced and used throughoutthe course as the favored modeling language. This requires the attendees to have a sound knowledge of atleast one object-oriented programming language (preferably Java).During the exercises, a running example (embedded software in a technical gadget or device) is utilizedand a team-based elaboration of the tasks is encouraged. Exercises cover tasks like the elicitation of re-quirements, definition of a design and eventually the implementation of executable (proof-of-concept)code.

2 Learning objectives / Learning OutcomesThis lecture aims to introduce basic software engineering techniques - with recourse to a set of best-practiceapproaches from the engineering of software systems - in a practice-oriented style and with the help of onerunning example.After attending the lecture students should be able to uncover, collect and document essential requirementswith respect to a software system in a systematic manner using a model-driven/centric approach. Further-more, at the end of the course a variety of means to acquiring insight into a software system’s design(architecture) should be at the student’s disposal.

3 Recommended prerequisite for participationsound knowledge of an object-oriented programming language (preferably Java)





6 Usability of this moduleBSc ETiT, BSc iST, BSc Wi-ETiT


8 Referenceswww.es.tu-darmstadt.de/lehre/se-i-v/

Courses

Course Nr. Course name18-su-1010-vl Software Engineering - Introduction

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Lecture 3

Course Nr. Course name18-su-1010-ue Software Engineering - Introduction

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr, M.Sc. Lars Fritsche Practice 1


http://www.es.tu-darmstadt.de/lehre/se-i-v/

Module nameSoftware-Engineering - Maintenance and Quality Assurance



1 ContentThe lecture covers advanced topics in the software engineering field that deal with maintenance and qual-ity assurance of software. Therefore, those areas of the software engineering body of knowledge whichare not addressed by the preceding introductory lecture, are in focus. The main topics of interest are:software maintenance and reengineering, configuration management, static programme analysis and met-rics, dynamic programme analysis and runtime testing as well as programme transformations (refactoring).During the exercises, a suitable Java open source project has been chosen as running example. The partic-ipants analyze, test and restructure the software in teams, each dealing with different subsystems.

2 Learning objectives / Learning OutcomesThe lecture uses a single running example to teach basic software maintenance and quality assuring tech-niques in a practice-oriented style. After attendance of the lecture a student should be familiar with allactivities needed to maintain and evolve a software system of considerable size. Main emphasis is laidon software configuration management and testing activities. Selection and usage of CASE tool as well asworking in teams in conformance with predefined quality criteria play a major role.

3 Recommended prerequisite for participationIntroduction to Computer Science for Engineers as well as basic knowledge of Java





6 Usability of this moduleMSc ETiT, MSc iST, MSc Wi-ETiT, Informatik


8 Referenceswww.es.tu-darmstadt.de/lehre/se_ii/

Courses

Course Nr. Course name18-su-2010-vl Software-Engineering - Maintenance and Quality Assurance

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr, M.Sc. Sebastian Marvin Ruland Lecture 3

Course Nr. Course name18-su-2010-ue Software-Engineering - Maintenance and Quality Assurance

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr, M.Sc. Sebastian Marvin Ruland Practice 1


http://www.es.tu-darmstadt.de/lehre/se_ii/

2.2 ADP / Seminars, Labs, CS-ES-NS

2.2.1 ADP / Seminars

2.2.1.1 Projekt Seminars Robotics

Module nameProject Seminar Learning Robots for Mechatronics



1 ContentCurrent research problems will be explored by groups of students in this class.In this project seminar, students will pose a current research problem in the domain of robot learning withassistance of their advisor. The students will select a robot learning topic to fit their research interests, onwhich they will pursue in-depth literature studies. Using these results, they will present a plan for theirproject, try out the algorithms of interest and implement a prototype in simulation.

2 Learning objectives / Learning OutcomesAfter attending the course, students will understand based on their own experience how machine learningalgorithms can be applied in robotics. They know several possible solvable tasks and can solve such taskswith learning algorithms from literature or their own design as a team.

3 Recommended prerequisite for participationRecommended:Prior participation in the lecture “Machine Learning for Robotics & Mechatronics”


• [20-00-1112-pp] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-1112-pp] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 References

Courses

Course Nr. Course name20-00-1112-pp Project Seminar Learning Robots for Mechatronics

Instructor Type SWSProf. Dr. rer. nat. Michael Waidner Project Seminar 6

2.2 ADP / Seminars, Labs, CS-ES-NS 64

Module nameRobotics Project Seminar for Mechatronics

Module Nr. Credit Points Workload Self study Duration Cycle offered20-00-1114 8 CP 240 h 150 h 1 Every Sem.


1 ContentIndependent processing and implementation of a complex problem in the field of research, developmentand validation of mechatronic systems in robotics (e.g. for mobile, stationary or wearable robotic systems)under scientific guidance:- Familiarization with the task and the state of the art in research and technology,- Analysis of objectives and requirements,- Development and implementation of a solution approach,- Evaluation of the developments and results- Documentation of task definition, solution approach, implementation and results in a final report- Realization of a final presentation

2 Learning objectives / Learning OutcomesAfter successful participation, the students are able to identify and analyze complex problems in researchand development of mechatronic systems in robotics, individually or in a team, as well as to develop,implement and evaluate possible solutions. They master the basics of work and time planning for complextasks.

3 Recommended prerequisite for participationRecommended: Successful participation in the course “Foundations of Robotics for Mechatronics” or equiv-alent knowledge and skills


• [20-00-1114-pp] (Study Achievement, Written/Oral Examination, Standard BWS)


• [20-00-1114-pp] (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 References

Courses

Course Nr. Course name20-00-1114-pp Robotics Project Seminar for Mechatronics

Instructor Type SWSProf. Dr. rer. nat. Michael Waidner Project Seminar 6


Module nameProject Seminar Robotics and Computational Intelligence



1 ContentThe following topics are taught in the lecture:Industrial robots

• Types and applications• Geometry and kinematics• Dynamic model• Control of industrial robots

Mobile robots• Types and applications• Sensors• Environmental maps and map building• Trajectory planning

Group projects are arranged in parallel to the lectures in order to apply the taught material in practicalexercises.

2 Learning objectives / Learning OutcomesAfter attending the lecture, a student is capable of: 1. recalling the basic elements of industrial robots, 2.recalling the dynamic equations of industrial robots and be able to apply them to describe the dynamics ofa given robot, 3. stating model problems and solutions to standard problems in mobile robotics, 4. planinga small project, 5. organizing the work load in a project team, 6. searching for additional backgroundinformation on a given project, 7. creating ideas on how to solve problems arising in the project, 8. writingan scientific report about the outcome of the project 8. presenting the results of the project.



• Module Examination (Study Achievement, Optional, Standard Grading System)


• Module Examination (Study Achievement, Optional, Weighting: 100 %)



8 ReferencesAdamy: Lecture notes (available for purchase at the FG office)

Courses

Course Nr. Course name18-ad-2070-pj Project Seminar Robotics and Computational Intelligence

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Project Seminar 4


2.2.1.2 Further Projekt Seminars

Module nameADP (6 CP) Applied Dynamics

Module Nr. Credit Points Workload Self study Duration Cycle offered16-25-a061 6 CP 180 h 180 h 1 WiSe/SoSe

Language Module ownerGerman Prof. Dr. Richard Markert

1 ContentCurrent research topic from the general area of the administering institute.

2 Learning objectives / Learning OutcomesThe students become acquainted with teamwork and are able to take over responsibility for leading taskswithin the team. They learn to assess divergent positions and the necessity of common agreements ininterpersonal relationships as well as typical engineering challenges in a positive manner. They are able torecognize and specify complex problems and to distinguish between different solutions. They also studyhow to valuate the importance of an exact time and work schedule positively.

3 Recommended prerequisite for participationPossible prerequisites will be prescribed by the individual institute supervising the project.


• Module Examination (Technical Examination, Technical Examination, Standard Grading System)





8 Referenceswill depend on topic; available upon announcement

Courses


Module nameADP (6 CP) Dynamics and Vibrations


Language Module ownerGerman Prof. Dr.-Ing. Peter Hagedorn











Courses


Module nameADP (6 CP) Automotive Engineering


Language Module ownerGerman Prof. Dr. rer. nat. Hermann Winner











Courses


Module nameADP (6 CP) Mechatronic Systems in Mechanical Engineering













Courses


Module nameADP (6 CP) Product Development and Machine Elements


Language Module ownerGerman Prof. Dr.-Ing. Herbert Birkhofer











Courses


Module nameADP (6 CP) System Reliability, Adaptive Structures and Machine Acoustics


Language Module ownerGerman Prof. Dr.-Ing. Tobias Melz











Courses


Module nameADP (6 CP) Internal Combustion Engines and Powertrain Systems


Language Module ownerGerman Prof. Dr. techn. Christian Beidl











Courses


Module nameApplication, Simulation and Control of Power Electronic Systems

Module Nr. Credit Points Workload Self study Duration Cycle offered18-gt-2030 8 CP 240 h 180 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Gerd Griepentrog

1 ContentIn an introductory meeting topics according to power electronics and control of drives are given to thestudents. During the seminary problems can be treated concerning the following topics:

• Simulation of power electronic systems plus analysis and evaluation of the models• Implementing and startup of power electronic systems, test stand development plus measurement of

characteristic parameters• Modeling and simulation in the field of control of electrical drives• Implementing and startup of controlled drive systems• Suggested topics from the students are welcome

The students are working autonomous on the chosen problem. The results are documented in a writtenreport and at the end of the module, a presentation about the problem must be held.

2 Learning objectives / Learning OutcomesThe Competences are:

• Autonomous familiarization with a given problem• Selection and evaluation of appropriate development tools• Familiarization with the used development tools• Practical experience in power electronics and control of drives• Logical presentation of the results in a report• Presentation skills

3 Recommended prerequisite for participationLecture „Leistungselektronik 1“ or „Einführung Energietechnik“ and ggf. „Regelungstechnik I“ or similar





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc MEC


8 ReferencesDefinition of project task

Courses

Course Nr. Course name18-gt-2030-se Application, Simulation and Control of Power Electronic Systems

Instructor Type SWSProf. Dr.-Ing. Gerd Griepentrog, M.Sc. Pavel Makin Seminar 4


Module nameProduct Development Methodology I



1 ContentPractical experience in the methods used for the development of technical products. Work in a projectteam.

2 Learning objectives / Learning OutcomesApplying the development methodology to a specific development project in a team. To do this, studentscan create a schedule, can analyze the state of the art, can compose a list of requirements, can abstractthe task, can work out the sub-problems, can seek solutions with different methods, can work out optimalsolutions using valuation methods, can set up a final concept, can derive the parameters needed by compu-tation and modeling, can create the production documentation with all necessary documents such aspartlists, technical drawings and circuit diagrams, can build up and investigate a laboratory prototype and canreflect their development in retrospect.

3 Recommended prerequisite for participationParallel attendance of Proseminar ETiT Option MPE





6 Usability of this moduleBSc ETiT, BSc WI-ETiT


8 ReferencesScript: Development Methodology (PEM)

Courses

Course Nr. Course name18-kn-1025-pj Product Development Methodology I

Instructor Type SWSProf. Dr. Mario Kupnik, Prof. Dr.-Ing. Khanh Quoc Tran, Prof. Dr.-Ing. KlausHofmann, Prof. Ph.D. Thomas Peter Burg

Project Seminar 3


Module namePlanning and Application of Electrical Drives (Drives for Electric Vehicles)

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2120 5 CP 150 h 120 h 1 SoSe

Language Module ownerGerman Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentMono- and hybrid drive concepts, motor technology, DC and AC machines, drive systems, car dynamic,energy storage;Seminary work: simulation of car with electric drive train, presentation of seminary work

2 Learning objectives / Learning OutcomesKnowledge on design proceduces for electric modulation systems for electric and hybrid cars

3 Recommended prerequisite for participationBachelor in Electrical Engineering or Mechatronics, “Electrical Drives and Machines” and "Power electron-ics" recommended





6 Usability of this moduleMSc ETiT, MSc MEC, MSc EPE, MSc WI-ETiT


8 ReferencesTextbook; Binder, A.: Electric machines and drives I, Darmstadt Univ. of TechnologyMitschke, M.: Dynamik der Kraftfahrzeuge, Springer Verlag Berlin

Courses

Course Nr. Course name18-bi-2120-se Planning and application of electrical drives (Drives for electric vehicles)

Instructor Type SWSProf. Harald Neudorfer Seminar 2


Module nameProject Seminar Automatic Control Systems



1 ContentThe students work in small groups, supervised by a scientific staff member, on individual problems takenfrom the field of automatic control. A compulsory training course is part of the project course and willcover the topics 1. team work and project management, 2. professional presentation skills, and 3. scientificwriting skills.

2 Learning objectives / Learning OutcomesAfter attending the project course, a student is capable of: 1. planing a small project, 2. organizing the workwithin a project team, 3. searching for scientific background information on a given project, 4. creatingideas on how to solve problems arising in the project, 5. presenting the results in a scientific report, and 6.giving a talk on the results of the project.



• Module Examination (Study Achievement, Oral Examination, Duration: 30 min, Standard GradingSystem)


• Module Examination (Study Achievement, Oral Examination, Weighting: 100 %)



8 ReferencesTraining course material

Courses

Course Nr. Course name18-ad-2080-pj Project Seminar Automatic Control Systems

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy Project Seminar 4


Module nameAutonomous Driving Lab I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-su-2070 6 CP 180 h 135 h 1 WiSe


1 Content• Hands-on programming experience with C++ in the development of embedded software systems

for autonomous driving based on a model car• Application of control methods from the area of autonomous driving• Application of software engineering techniques (design, documentation, test, ...) of a non-trivial

embedded software system with hard real-time requirements and limited resources (memory, ...)• Use of a given software framework and further libraries including a modular (real-time) operating

system• Hands-on experience using source code management systems, time management and other project

management tools• Presentations of the project results

2 Learning objectives / Learning OutcomesDuring this project seminar students gain practical experience in software development for embeddedsystems in the field of autonomous driving using a model car. In teamwork, they learn to cope with anextensive task. In order to solve this task they practice to use the theoretical knowledge available in thegroup (from other courses such as real-time systems, software engineering - introduction, C++ lab, digitalcontrol systems).Students that have successfully participated in this project seminar are able to organize and set-up a non-trivial software project in an interdisciplinary team according to a given problem independently. Theparticipants acquire the following skills in detail:

• Independent familiarization with a given software framework and ready-made libraries• Transfer of theoretic knowledge into a software system• Extensive use of tools for version, configuration, and change management• Realistic time and resource management (project management)• Development of hardware/software systems with C++ considering important limitations of embed-

ded systems• Planning and implementation of extensive quality assurance measures• Collaboration and communication in and between teams

3 Recommended prerequisite for participationRecommended prerequisites are:

• ETiT/DT, iST, Informatik, WI-ET/DT: Basic software technology knowledge and advanced knowledgeof object-oriented programming languages (especially C++)

Additionally desired:• Basic knowledge of the development of real-time systems or image processing• ETiT/AUT, MEC: Basic knowledge in control engineering including state space control design, some

additional basic knowledge in digital control design may be helpful



5 Grading


Module Final Examination:• Module Examination (Study Achievement, Oral Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, BSc iST


8 Referenceshttps://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-i/ and Moodle

Courses

Course Nr. Course name18-su-2070-pj Autonomous Driving Lab I

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr, Dr. Ing. Eric Lenz, M.Sc. Stefan Tomaszek Project Seminar 3


https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-i/

Module nameEnergy Converters and Electric Drives

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2130 6 CP 180 h 135 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentFrom the topics of proposed scientific theses, subtasks are derived. Groups of two to four students willwork on these subtasks under supervision of a tutor. The focus of the work can be either theo-retical orexperimental and contains scientific problems in the field of electric energy conversion and electric drives.For study program Mechatronics this corresponds to the Advanced Design Project.

2 Learning objectives / Learning OutcomesEnergy Converters, Electric Drives, Control of Electric Drives, Teamwork, Writing Scientific Reports, Pre-sentation

3 Recommended prerequisite for participationFundamentals on Electrical Engineering, Three-phase Systems, Mechanics; Lecture „Electrical Machinesand Drives“





6 Usability of this moduleMSc MEC, MSc ETiT,MSc EPE


8 ReferencesDepending on the project task; manuscripts from the lectures „Electrical Machines and Drives“, „Motordevelopment for electric Drive Systems“, „Regelungstechnik 1“

Courses

Course Nr. Course name18-bi-2130-pj Energy Converters and Electric Drives

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Project Seminar 3


Module nameProject seminar Advanced Applications of Lighting Engineering

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kh-2052 5 CP 150 h 105 h 1 WiSe/SoSe

Language Module ownerGerman Prof. Dr.-Ing. Khanh Quoc Tran

1 ContentFor the project seminar a question from the following topics can be worked on: automotive lighting,light for the autonomous car, interior lighting, exterior lighting; smart lighting, human centric lighting(hcl); horticultural lighting; generation, perception and cognition of the visual stimulus (luminaires, dis-plays, projection); LED/OLED technology; physical and psychophysical light measurement; illuminatingengineering, color perception, virtual reality tests for light-simulation.

2 Learning objectives / Learning OutcomesThe objective of this project seminar is the practical implementation of the knowledge acquired duringthe study in the form of a project work. Students participate on their own or in a team. In this projectseminar, students learn to plan, implement and validate lighting issues. The basics of the lecture and theproject seminar ‘Applications of Lighting Engineering’ are applied and deepened. This usually includes theselection of suitable illuminants, the development of electronic hardware as well as the use of photometricmeasuring instruments. In addition, the students learn how to abstract questions, communicate project-dependent information as well as present and discuss results.

3 Recommended prerequisite for participationLighting Technology I-II (desireable), Project seminar Applications of Lighting Engineering


• Module Examination (Study Achievement, Written/Oral Examination, Standard Grading System)To conclude the project, every student has to hold a presentation with a short round of questions and an-swers and also to deliver a written report about the work and the results.The presentation with exam and the report will be graded according to the fixed guidelines of our Labora-tory.


• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 100 %)



8 ReferencesLecture notes of Lighting Technology I (Khanh); Lecture slides of our Laboratory; Book "LED Lighting:Technology and Perception" (Khanh et al., Wiley); Book „Farbwiedergabe" (Khanh et al., Pflaum-Verlag);specific literature depending on the topic, publications.

Courses

Course Nr. Course name18-kh-2052-pj Project seminar Advanced Applications of Lighting Engineering

Instructor Type SWSProf. Dr.-Ing. Khanh Quoc Tran Project Seminar 3


Module nameProject seminar Applications of Lighting Engineering

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kh-2051 5 CP 150 h 105 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Khanh Quoc Tran

1 ContentThe project seminar deals with the following subjects: automotive lighting, interior lighting, exteriorlighting; generation, perception and cognition of the visual stimulus (luminaires, displays, projection);LED/OLED technology; physical and psychophysical light measurement; illuminating engineering, colorperception.

2 Learning objectives / Learning OutcomesThe objective of this project seminar is the practice oriented implementation of the material learned duringthe lectures in form of a project work. Via communication of the interdisciplinary way of thinking of thelighting engineer, students should carry out autonomous project work on their own or in a team.

3 Recommended prerequisite for participationLighting Technology I-II (desireable)





6 Usability of this moduleMSc ETiT, MSc iST, MSc WI-ETiT, MSc MEC, MSc MPE, MSc Phys


8 ReferencesLecture notes of Lighting Technology I (Khanh); Lecture slides of our Laboratory; Book “LED Lighting:Technology and Perception” (Khanh et al., Wiley); Book „Farbwiedergabe“ (Khanh et al., Pflaum-Verlag);specific literature depending on the topic, publications.

Courses

Course Nr. Course name18-kh-2051-pj Project seminar Applications of Lighting Engineering

Instructor Type SWSProf. Dr.-Ing. Khanh Quoc Tran Project Seminar 3


Module nameProject Seminar MFT



1 ContentConsists of „Product Development Methodology I” and “Proseminar ETIT Option MPE”. Intense theoreticaland practical engagement with development methodology as an individual, but also within a project groupat a specific didactic meaningful example.

2 Learning objectives / Learning OutcomesStudents learn the five major stages of development methodology and apply it to a specific developmentproject. In addition, tools for project planning and resource allocation, issues and assistance for produc-tive team work and knowledge to successfully create technical reports and presentations are learned andtrained.



• Module Examination (Study Achievement, Written/Oral Examination, Standard Grading System)• Module Examination (Study Achievement, Written/Oral Examination, Standard Grading System)


• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 5)• Module Examination (Study Achievement, Written/Oral Examination, Weighting: 2)

6 Usability of this moduleMSc MEC


8 References

Courses

Course Nr. Course name18-kn-1025-pj Product Development Methodology I

Instructor Type SWSProf. Dr. Mario Kupnik, Prof. Dr.-Ing. Khanh Quoc Tran, Prof. Dr.-Ing. KlausHofmann, Prof. Ph.D. Thomas Peter Burg

Project Seminar 3

Course Nr. Course name18-kn-1000-ps Proseminar ETiT

Instructor Type SWSProf. Dr. Mario Kupnik Introductory

Seminar Course2


Module nameMultimedia Communications Project I

Module Nr. Credit Points Workload Self study Duration Cycle offered18-sm-1030 9 CP 270 h 210 h 1 WiSe/SoSe

Language Module ownerGerman and English Prof. Dr.-Ing. Ralf Steinmetz

1 ContentThe course deals with cutting edge scientific and development topics in the area of multimedia communica-tion systems. Besides a general overview, it provides a deep insight into a special scientific topic. The topicsare selected according to the specific working areas of the participating researchers and convey technicaland scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia/e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows• Resource-based Learning

2 Learning objectives / Learning OutcomesThe ability to solve and evaluate technical problems in the area of design and development of futuremultimedia communication networks and applications using state of the art scientific methods. Acquiredcompetences are among the following:

• Searching and reading of project relevant literature• Design of communication applications and protocols• Implementing and testing of software components• Application of object-orient analysis and design techniques• Acquisition of project management techniques for small development teams• Evaluation and analyzing of technical scientific experiments• Writing of software documentation and project reports• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to develop and explore challenging solutions and applications in cutting edge multimediacommunication systems. Further we expect:

• Basic experience in programming Java/C# (C/C++).• Basic knowledge in Object oriented analysis and design.• Knowledge in computer communication networks. Lectures in Communication Networks I and/or

Net Centric Systems are recommended.






6 Usability of this moduleBSc ETiT, BSc/MSc iST, MSc MEC, Wi-CS, Wi-ETiT, BSc/MSc CS


8 ReferencesEach topic is covered by a selection of papers and articles. In addition we recommend reading of selectedchapters from following books:

• Andrew Tanenbaum: “Computer Networks”. Prentice Hall PTR (ISBN 0130384887)• Raj Jain: "The Art of Computer Systems Performance Analysis: Techniques for Experimental Design,

Measurement, Simulation, and Modeling" (ISBN 0-471-50336-3)• Erich Gamma, Richard Helm, Ralph E. Johnson: "Design Patterns: Objects of Reusable Object Ori-

ented Software" (ISBN 0-201-63361-2)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-1030-pj Multimedia Communications Project I

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz, M.Sc. Julian Zobel, M.Sc. Daniel Bischoff, M.Sc.Tim Steuer

Project Seminar 4


Module nameProject Course Practical Application of Mechatronics



1 ContentTeams of 2-4 students work on different mechatronic projects under the guidance of a project coordinatorfrom the institute.The projects mainly cover the following subject areas:

• Modeling, analysis, and design of mechatronic systems• Robust control design• System analysis, supervision and fault diagnosis• Modeling and identification

Application areas are mechatronic actuators, machine tools, production lines, test benches, automobiles,quadrocopters.

2 Learning objectives / Learning OutcomesAfter completing the project, the students will be familiar with the individual steps of investigating amechatronic project. This includes in particular the compilation of a system specification as well as criticaldiscussions and systematic selection of appropriate mechatronic solutions and their real technical imple-mentation. Doing so, the students learn the practical application of mechatronic methods taught in thelectures to real world problems. Additionally, in this project course, the students are supposed to im-prove their professional skills. These skills include e.g. teamwork, presentation techniques and systematicinformation retrieval.

3 Recommended prerequisite for participationLectures „System Dynamics and Automatic Control Systems I“, „System Dynamics and Automatic ControlSystems II“





6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST


8 ReferencesHandouts will be distributed at start of the project (e.g. hints for writing project documentation, etc.)

Courses

Course Nr. Course name18-ko-2130-pj Project Course Practical Application of Mechatronics

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski, M.Sc. Julian Zeiß Project Seminar 4


Module nameProject Course Control Engineering



1 ContentTeams of 2 - 4 students work on different control engineering projects under the guidance of a projectcoordinator from the institute. The projects mainly cover the following subject areas:

• Modelling, analysis and design of multivariable control systems• Modelling, analysis and design of distributed parameter systems• Robust control design• System analysis, supervision and fault diagnosis• Modelling and identification

Application areas are machine tools, production lines, test benches, process control, automobiles.

2 Learning objectives / Learning OutcomesAfter completing the project the students will be familiar with the individual steps of investigating a controlengineering project. This includes in particular the compilation of a system specification as well as criticaldiscussions and systematic selection of appropriate control engineering solutions and their real technicalimplementation. Doing so the students learn the practical application of control engineering methodstaught in the lecture “System Dynamics and Control Systems I” to real world problems. Additionally, inthis project course the students are supposed to improve their professional skills. These skills include e.g.teamwork, presentation techniques and systematic information retrieval.

3 Recommended prerequisite for participationLecture “System Dynamics and Control Systems I”







8 ReferencesHandouts will be distributed at start of the project (e.g. Hints for writing a project documentation, etc.)

Courses

Course Nr. Course name18-ko-2090-pj Project Course Control Engineering

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski Project Seminar 4


Module nameMultimedia Communications Seminar I



1 ContentThe seminar investigates current and upcoming topics in multimedia communication systems, which areexpected to be of utmost importance for the future evolution of the Internet and information technolgyin goal. The goal is to learn more about multimedia communication systems by studying, summarizing,and presenting top quality papers from recent high quality networking research journals, magazines, orconferences. The selection of topics corresponds to the research area of participating researchers.Possible topics are:

• Knowledge & Educational Technologies• Self organizing Systems & Overlay Communication• Mobile Systems & Sensor Networking• Service-oriented Computing• Multimedia Technologies & Serious Games

2 Learning objectives / Learning OutcomesThe students are actively studying cutting edge scientific articles, standards, and books about multimediacommunication systems and applications, which are expected to be of utmost important for the future ofthe Internet.Students acquire competences in the following areas:

• Searching and reviewing of relevant scientific literature• Analysis and evaluation of complex technical and scientific information• Writing of technical and scientific summaries and short papers• Presentation of complex technical and scientific information






6 Usability of this moduleCS, WiCS, ETiT, Wi-ETiT, BSc/MSc iST


8 ReferencesDepending on specific topic (selected articles of journals, magazines, and conferences).

Courses

Course Nr. Course name18-sm-2300-se Multimedia Communications Seminar I

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz, M.Sc. Julian Zobel, M.Sc. Daniel Bischoff, M.Sc.Tim Steuer

Seminar 3


Module nameSeminar Software System Technology



1 ContentIn this course, the students produce scientific reports from changing subject areas. Each student has toexplore a subject related to IT system development and produce a written report as well as a final talk with apresentation. A list of the subjects of the current semester is available at www.es.tu-darmstadt.de/lehre/sst.

2 Learning objectives / Learning OutcomesAfter a successful participation, the students will be able to explore an unknown topic under scientificaspects. The students learn to support the exploration by a literature research and to analyze the subjectcritically. They achieve the skills to present a definite subject in a written report as well as in an oralpresentation.

3 Recommended prerequisite for participationBasic knowledge in software engineering and programming languages





6 Usability of this moduleBSc iST, BSc Informatik, MSc ETiT


8 Referenceswww.es.tu-darmstadt.de/lehre/sst

Courses

Course Nr. Course name18-su-2080-se Seminar Software System Technology

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr Seminar 2


http://www.es.tu-darmstadt.de/lehre/sst

http://www.es.tu-darmstadt.de/lehre/sst

2.2.2 Labs

Module nameAdvanced Integrated Circuit Design Lab

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ho-2120 6 CP 180 h 135 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Klaus Hofmann

1 ContentPractical Design Tasks in Full Custom Design of Digital or Analog Ciruits using State-of-the-Art CommercialCAD Tools

2 Learning objectives / Learning OutcomesA student is, after successful completion of this module, able to 1. develop and verify transistor circuitryusing Cadence 2. simulate logic and analog circuits (Pre- and Postlayout) 3. draw, verify and extract layout

3 Recommended prerequisite for participationLecture “Advanced Digital Integrated Circuit Design” or “Analog Integrated Circuit Design”





6 Usability of this moduleMSc ETiT, MSc Wi-ETiT, MSc iCE, MSc iST, MSc MEC, MSc EPE


8 ReferencesADIC Lecture Slide Copies; John P. Uyemura: Fundamentals of MOS Digital Integrated Circuits; Neil Westeet al.: Principles of CMOS VLSI Design

Courses

Course Nr. Course name18-ho-2120-pr Advanced Integrated Circuit Design Lab

Instructor Type SWSProf. Dr.-Ing. Klaus Hofmann Internship 3


Module namePractical Training with Drives


Language Module ownerGerman and English Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentThe purpose of this laboratory is gaining extented knowledge about realization and behaviour of drive sys-tems. An introduction in measurement problems concerning drives is given. The contents of the laboratoryis setting drives to work and investigating drive systems under laboratory conditions. Special attention ispaid to inverter-fed AC drives. The laboratory experiments are individually coordinated with the previousknowledge of the respective courses (ETiT or MEC).

2 Learning objectives / Learning OutcomesThe students get the ability of measurement for electrical motors, generators and transformers.

3 Recommended prerequisite for participationBachelor of Science in Electrical Engineering, Power Engineering or similar





6 Usability of this moduleMSc ETiT, MSc MEC, MSc WI-ETiT


8 ReferencesTextbook with lab instructions;Nürnberg, W.: Die Prüfung elektrischer Maschinen, Springer, 2000;Leonhard, W.: Control of electric drives, Springer, 2000;Textbook – Binder, A.: Motor Developement for Electrical Drive Systems; Lecture notes – Mutschler, P.:Control of Drives

Courses

Course Nr. Course name18-bi-2100-pr Practical Training with Drives

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Internship 3

Course Nr. Course name18-bi-2090-tt Laboratory Briefing

Instructor Type SWSProf. Dr. techn. Dr.h.c. Andreas Binder Tutorial 0


Module nameMechatronics Workshop



1 ContentDuring the mechatronic workshop students get the possibility to design and construct their own fixture,which contains a ball track and a ball elevator mechanism. Herefore dimensional plans have to be un-derstood correctly. Afterwards all components (i.e. circuit board, rails and holders) have to be designedand manufactured within the electronic lab and the workshop, where students work independently withturning, drilling and milling machines.The mechatronic workshop allows students to gain practical experience and knowledge in contruction,assembling and PCB layout design.

2 Learning objectives / Learning OutcomesUnderstanding of construction plans, circuit layout design, practical experience with turning, drilling andmilling machines.

3 Recommended prerequisite for participationYou have to bring your own printed copy of the script. This is mandatory for attending the course. Thescript will be published on the moodle platform.





6 Usability of this moduleBSc/MSc ETiT, BSc/MSc MEC


8 References• Lecture Notes „Mechatronics Workshop“• J. Dillinger et al.: Fachkunde Metall, Europa-Lehrmittel, 2007• U. Tietze, C. Schenk, E. Gamm: Halbleiter-Schaltungstechnik, Springer, 2012

Courses

Course Nr. Course name18-bi-1050-pr Mechatronics Workshop



Module nameElectromechanical Systems Lab

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kn-2090 4 CP 120 h 75 h 1 SoSe


1 ContentElectromechanical sensors, drives and actuators, electronic signal processing mechanisms, systems fromactuators, sensors and electronic signal processing mechanism.

2 Learning objectives / Learning OutcomesElaborating concrete examples of electromechanical systems, which are explained within the lectureEMS I+II.The Analyzing of these examples is needed to explain the mode of operation and to gather characteristicvalues. On this students are able to explain the derivative of proposals for the solution.The aim of the 6 laboratory experiments is to get to know the mode of operation of the electro- mechan-ical systems. The experimental analysis of the characteristic values leads to the derivation of proposedsolutions.

3 Recommended prerequisite for participationBachelor ETiT





6 Usability of this moduleMSc ETiT, MSc WI-ETiT, MSc MEC


8 ReferencesLaboratory script in Electromechanical Systems

Courses

Course Nr. Course name18-kn-2090-pr Electomechanical Systems Lab

Instructor Type SWSProf. Dr. Mario Kupnik Internship 3

Course Nr. Course name18-kn-2090-ev Electomechanical Systems Lab - Introduction

Instructor Type SWSProf. Dr. Mario Kupnik Introductory

Course0


Module nameLaboratory Matlab/Simulink II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-2070 4 CP 120 h 60 h 1 WiSe/SoSe


1 ContentThe lab is split into the two parts Simulink and Control Engineering II. First the fundamentals of the simu-lation tool Simulink are introduced and their application to problems from different fields of application istrained. In the second part, the knowledge gained in the first part is applied to autonomously solve severalcontrol design problems as well as simulation tasks.

2 Learning objectives / Learning OutcomesThe students will be able to work with the tool MatLab/Simulink on their own and can solve tasks fromthe areas of control engineering and numericial simulation. The students will know the different designmethods of the control system toolbox and the fundamental concepts of the simulation tool Simulink. Theycan practically apply the knowledge gathered in the lectures “System Dynamics and Control Systems I andII” and “Modelling and Simulation”.

3 Recommended prerequisite for participationThe lab should be attended in parallel or after the lectures “System Dynamics and Control Systems II” and“Modelling and Simulation”





6 Usability of this moduleMSc ETiT, MSC MEC


8 ReferencesLecture notes for the lab tutorial can be obtained at the secretariat

Courses

Course Nr. Course name18-ko-2070-pr Laboratory Matlab/Simulink II

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski, M.Sc. Marcel Bonnert Internship 4


Module nameMultimedia Communications Lab I



1 ContentThe course deals with cutting edge development topics in the area of multimedia communication systems.Beside a general overview it provides a deep insight into a special development topic. The topics areselected according to the specific working areas of the participating researchers and convey technical andbasic scientific competences in one or more of the following topics:

• Network planning and traffic analysis• Performance evaluation of network applications• Discrete event simulation for network services• Protocols for mobile ad hoc networks / sensor networks• Infrastructure networks for mobile communication / mesh networks• Context-aware communication and services• Peer-to-peer systems and architectures• Content distribution and management systems for multimedia/e-learning• Multimedia authoring and re-authoring tools• Web service technologies and service-oriented architectures• Applications for distributed workflows• Resource-based Learning

2 Learning objectives / Learning OutcomesThe ability to solve simple problems in the area of multimedia communication shall be acquired. Acquiredcompetences are:

• Design of simple communication applications and protocols• Implementing and testing of software components for distributed systems• Application of object-oriented analysis and design techniques• Presentation of project advances and outcomes

3 Recommended prerequisite for participationKeen interest to explore basic topics of cutting edge communication and multimedia technologies. Furtherwe expect:

• Basic experience in programming Java/C# (C/C++).• Knowledge in computer communication networks. Lectures in Communication Networks I and/or

Net Centric Systems are recommended.





6 Usability of this moduleBSc ETiT, BSc/MSc iST, MSc MEC, Wi-CS, Wi-ETiT, BSc/MSc CS


8 References


Each topic is covered by a selection of papers and articles. In addition we recommend reading of selectedchapters from following books:

• Andrew Tanenbaum: “Computer Networks”. Prentice Hall PTR (ISBN 0130384887)• Christian Ullenboom: "Java ist auch eine Insel: Programmieren mit der Java Standard Edition Version

5 / 6" (ISBN-13: 978-3898428385)• Kent Beck: "Extreme Programming Explained - Embrace Changes" (ISBN-13: 978-0321278654)

Courses

Course Nr. Course name18-sm-1020-pr Multimedia Communications Lab I

Instructor Type SWSProf. Dr.-Ing. Ralf Steinmetz, M.Sc. Daniel Bischoff, M.Sc. Tim Steuer Internship 3


Module nameLaboratory Control Engineering II



1 ContentDuring the laboratory course the following experiments will be conducted: Coupling control of a helicopter,Non-linear control of a gyroscope, Nonlinear multivariable control of an aircraft, Servo control systems,Control of an overhead crane system, Programmable logic control of a stirring process

2 Learning objectives / Learning OutcomesAfter attending this laboratory course, a student is capable of:

• recalling the basics of the conducted experiments,• organize and comprehend background information for experiments,• assemble experimental set-ups based on manuals,• judge the relevance of experimental results by comparing them with theoretically predicted out-

comes,• present the results of the experiments

3 Recommended prerequisite for participationSystem Dynamics and Control Systems II, the attendance of the additional lecture “System Dynamics andControl Systems III” is recommended


• Module Examination (Study Achievement, Written Examination, Duration: 180 min, Standard Grad-ing System)


• Module Examination (Study Achievement, Written Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc Wi-ETiT, Biotechnik


8 ReferencesAdamy: Instruction manuals for the experiments (available during the kick-off meeting)

Courses

Course Nr. Course name18-ad-2060-pr Laboratory Control Engineering II

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy, M.Sc. Jan Christian Zimmermann Internship 4


Module nameSoftware Lab

Module Nr. Credit Points Workload Self study Duration Cycle offered18-st-1020 4 CP 120 h 75 h 1 WiSe

Language Module ownerGerman Prof. Dr. rer. nat. Florian Steinke

1 ContentThe lab covers the following basic software development skills:

• working together and software development in teams• lightweight software engineering process eXtreme Programming (XP)• training of advanced OO/Java programming skills and coding standards• software documentation using JavaDoc• the basics of the development tool eclipse• regression testing methods (test framework JUnit) to increase software quality• more sophisticated data structures and algorithms

2 Learning objectives / Learning OutcomesStudents participating in the lab deepen their basic programming knowledge (acquired in Computer Sci-ence for Engineers). The focus is on development of “medium-size” software in contrast to programmingsmall toy examples, working in teams and evolution of existing software (framework). Afterwards studentsare expected to be able to develop small software systems using a "light-weight" software developmentprocess. Furthermore, they will appreciate training in more sophisticated software engineering techniquesneeded for the development of "real-world" software systems.

3 Recommended prerequisite for participationBasics in Java (as taught in Introduction to Computer Science for Engineers).Windows-Account of the ETiT PC-Pool





6 Usability of this moduleBSc ETiT, BSc Wi-ETiT


8 Referenceswww.es.tu-darmstadt.de/lehre/sp/

Courses

Course Nr. Course name18-st-1020-pr Software Lab

Instructor Type SWSProf. Dr. rer. nat. Florian Steinke Internship 3


http://www.es.tu-darmstadt.de/lehre/sp/

Module nameTutorial Introduction to Design of Experiments

Module Nr. Credit Points Workload Self study Duration Cycle offered16-26-5160 4 CP 120 h 120 h 1 Every Sem.

Language Module ownerEnglish Prof. Dr.-Ing. Tobias Melz

1 ContentThis tutorial teaches the basics of statistics and statistical experimental design. The approach to practicalstatistical analyses is learned by means of exercises. Relevant technical terms of statistics are taught andthe application of statistical methods is practiced. In addition, common errors in experimental design andexperimental evaluation are addressed. Finally, the methods are applied to an example system in groupwork. Here, the students’ knowledge is tested in practice.

2 Learning objectives / Learning OutcomesOn successful completion of this module, students should be able to:1. Apply the basic methods of statistical design of experiments.2. Identify uncontrollable disturbance variables and to statistically estimate their influence on consideredoutput variables3. Set up full and partial factorial experimental designs, to determine the resulting superimposed parame-ters and to transfer this knowledge to more complex experimental designs.4. Describe the basic procedure of hypothesis testing and the most important methods (T-test, F-test).5. Name possible solutions in case of violation of the mathematical requirements of hypothesis tests.

3 Recommended prerequisite for participationThe participation in the courses System Reliability in mechanical engineering and/or Reliability in mechan-ical engineering is recommended.


• Module Examination (Technical Examination, Special Form, Standard Grading System)Written work (50 %) and 20-minute presentation including a colloquium (50 %).


• Module Examination (Technical Examination, Special Form, Weighting: 100 %)



8 ReferencesSkript, Aufgabenstellungen, Ausdruck der Präsentationsfolien.Douglas C. Montgomery: Design and Analysis of Experiments, John Wiley & Sons, 2008.Lecture notes, problem sets, presentation slides.Douglas C. Montgomery: Design and Analysis of Experiments, John Wiley & Sons, 2008.

Courses

Course Nr. Course name16-26-5160-tt Tutorial Introduction to Design of Experiments

Instructor Type SWSTutorial 0


Module nameTutorial Automotive Engineering


Language Module ownerGerman Prof. Dr. rer. nat. Hermann Winner

1 ContentThe Automotive Engineering Tutorium deepens special topics from the courses Motor Vehicles I+II on thebasis of practically performed experiments. The selection of the experiments follows the availability oftesting vehicles or current problems.

2 Learning objectives / Learning OutcomesYou are able to make independent experiments with vehicles for a given problem. This comprises thedefinition of test procedures and measuring devices. Test parameters are definied and varied. You are ableto make use of the theoretical knowledge from Motor Vehicles I and II.

3 Recommended prerequisite for participationFundamentals of automotive engineering







8 Referencesmaterials are handed out to participants

Courses

Course Nr. Course name16-27-5080-tt Tutorial Automotive Engineering



Module nameTutorial on Flight Mechanics


Language Module ownerGerman Prof. Dr.-Ing. Uwe Klingauf

1 ContentMeasurements on ground; performance of test flights with a 2-seat motor glider supervised by a pilotinstructor: analysis of flight performance and handling qualities; test protocol and final report.

2 Learning objectives / Learning OutcomesStudents will be able to: determine flight performance and handling qualities based on measured data;know and judge performance and handling qualities of a motor glider based on own flight experience;judge capabilities and limitations of flight measurement techniques.

3 Recommended prerequisite for participationFlight Mechanics I and II








Courses

Course Nr. Course name16-23-5080-tt Tutorial on Flight Mechanics



Module nameTutorial Advanced Cax Methods

Module Nr. Credit Points Workload Self study Duration Cycle offered16-07-5100 4 CP 120 h 60 h 1 WiSe/SoSe

Language Module ownerGerman Prof. Dr. Reiner Anderl

1 ContentStudents gain knowledge of advanced CA Methods through the analysis of recent industrial examples. Thiscourse builds on the basic course ’Einführung in das rechnerunterstützte Konstruieren (CAD)’.

2 Learning objectives / Learning OutcomesThe students will be familiar with advanced CA Methods. They are able to recognise, execute and plan thegeneric workflow of CA Processes. Furthermore they are able to transfer their theoretical knowledge intoindustrial practice.

3 Recommended prerequisite for participationEinführung in das rechnergestützte Konstruieren (CAD)Virtuelle Produktentwicklung A, B, C







8 References

Courses

Course Nr. Course name16-07-5100-tt Tutorial Advanced CAx Methods



Module nameTutorial Basic Robot Programming


Language Module ownerGerman Prof. Dr.-Ing. Eberhard Abele

1 Content

2 Learning objectives / Learning Outcomes



• Module Examination (Technical Examination, Oral Examination, Standard Grading System)





8 References

Courses

Course Nr. Course name16-09-5180-tt Tutorial Basic Robot Programming



Module nameTutorial Machine Acoustics


Language Module ownerGerman Prof. Dr.-Ing. Holger Hanselka

1 ContentTheoretical and experimental determination of the acoustic response in machine structures, which areexcited by external forces (experimental and theoretical approach). To learn how to use modern acousticmeasuring equipment in order to determine and validate airborne and structure borne noise (Matlab andExcel).

2 Learning objectives / Learning OutcomesAfter this tutorial, students will be familiar with modern acoustical measurement equipment, the properapplication of the latest standards, guidelines and regulations in acoustics as well as the validation ofsimulation results.

3 Recommended prerequisite for participationModule “Maschine Acoustics - Fundamentals I”







8 ReferencesStudents will receive handouts which include the required information of the subject, formulas, directives,security information, appraisals, demands, data sheets and instruction manuals

Courses

Course Nr. Course name16-26-5100-tt Tutorial Machine Acoustics



Module nameTutorial Pneumatics


Language Module ownerGerman Prof. Dr.-Ing. Peter Pelz

1 Content









8 References

Courses

Course Nr. Course name16-10-5200-tt Tutorial Pneumatics I



2.2.3 CS-ES-NS

All modules previously listed in the open catalogue Additionals RoboticsADP / Seminars, Labs, CS-ES-NS

Module nameBasics of Economics for Engineers


Language Module ownerGerman Prof. Dr.-Ing. Joachim Metternich

1 ContentThis course is supposed to supply future engineers with fundamental knowledge in economics. This in-cludes the basics in accounting and the annual financial statement, in cost accounting as well as ineconomic efficiency calculation. Subsequently, relevant aspects concerning human resources, procure-ment management, logistics, marketing and strategic management are addressed. The provided content issupposed to prepare the students for their future professional life and especially for designing economicallyviable innovations. Practical examples from the industrial environment help understand the content.


• Explain the basics of cost calculation.• Orientate decisions in the areas of production, quality management, development, or purchasing on

economic criteria.• Describe the tasks of the technical purchase, the distribution as well as the technical marketing.• Explain processes of companies close to production and describe the approach to optimize the pro-

cesses.• Dicuss to graduates in business management and businessmen and make proper decisions in

companies close to production.



• Module Examination (Technical Examination, Technical Examination, Standard Grading System)Written exam 1 h 30 min





8 ReferencesLecture notes are available during the course and in PTW’s secretariat

Courses

Course Nr. Course name16-09-5050-vl Basics of Economics for Engineers



Module nameIntroduction to Numerical Analysis


Language Module ownerGerman Prof. Dr. rer. nat. Jens Lang

1 ContentCondition, systems of linear and nonlinear equations, least squaresminimization, interpolation, integration and differentiation, differentialequations, difference schemes, programming exercises.









8 References

Courses

Course Nr. Course name04-00-0056-vu Introduction to Numerical Analysis

Instructor Type SWSProf. Dr. rer. nat. Jens Lang Lecture & Prac-

tice6


Module nameElectric drives for cars

Module Nr. Credit Points Workload Self study Duration Cycle offered18-bi-2150 4 CP 120 h 75 h 1 WiSe

Language Module ownerEnglish Prof. Dr. techn. Dr.h.c. Andreas Binder

1 ContentThis course introduces the students to the different design aspects of electric drives used in automotiveapplications, comprising both high power density high speed traction and small mass produced auxiliarydrives. Since the target audience comprises students from different degree programmes, the course firstreviews basics of electromagnetic power conversion principles and design principles of PM based machines.The discussion of the electric drives themselves comprises the various facets of their design as part of acomplex system, such as operating requirements, configurations, material choices, parasitic effects andtheir mitigation, electric and thermal stress, as well as manufacturing related questions, notably as theyaffect the design of the mass produced auxiliary drives.

2 Learning objectives / Learning OutcomesAt the end of the course, the students will know about design principles of PM based machines, electricdrives: topologies, operating areas, dynamic performance and configuration of traction drives for hybridcars and electric vehicles as they apply to electric drives for cars. In addition to traction drives, they willalso be familiar with auxiliary drives used in cars. They will understand the parasitic effects of inverterinduced bearing currents, the insulation material used for the electric winding and the winding stress atinverter supply. They will be familiar with the different cooling principles and thermal modelling, as well asthe thermal aspects of the integration into the car. They will also know about the main failure modes thatmay occur with electric drives used for cars, the different lamination sheets used and their manufacturing.

3 Recommended prerequisite for participationCompleted Bachelor of Electrical Engineering or equivalent degree.







8 References

Courses

Course Nr. Course name18-bi-2150-vl Electric drives for cars

Instructor Type SWSProf. Dr. Annette Mütze Lecture 2

Course Nr. Course name18-bi-2150-ue Electric drives for cars

Instructor Type SWSProf. Dr. Annette Mütze Practice 1


Module nameMatrix Analysis and Computations

Module Nr. Credit Points Workload Self study Duration Cycle offered18-pe-2070 6 CP 180 h 120 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Marius Pesavento

1 ContentThis graduate course is a foundation class on matrix analysis and computations, which are widelyused in many different fields, e.g., machine learning, computer vision, systems and control, signal andimage processing, communications, networks, optimization, and many more. . .Apart from the theory this course will also cover the design of efficient algorithm and it considers manydifferent examples from the aforementioned fields including examples from social media and big dataanalysis, image processing and medical imaging, communication network optimization, and written textclassification.Specific topics: (i) basic matrix concepts, subspace, norms, (ii) linear least squares (iii) eigendecompo-sition, singular value decomposition, positive semidenite matrices, (iv) linear system of equations, LUdecomposition, Cholesky decomposition (v) pseudo-inverse, QR decomposition (vi) advanced tensor de-composition, advanced matrix calculus, compressive sensing, structured matrix factorization

2 Learning objectives / Learning OutcomesStudents will learn matrix analysis and computations at an advanced or research level.

3 Recommended prerequisite for participationBasic knowledge in linear algebra.







8 References1.Gene H. Golub and Charles F. van Loan, Matrix Computations (Fourth Edition), John Hopkins UniversityPress, 2013.2.Roger A. Horn and Charles R. Johnson, Matrix Analysis (Second Edition), Cambridge University Press,2012.3.Jan R. Magnus and Heinz Neudecker, Matrix Differential Calculus with Applications in Statistics andEconometrics (Third Edition), John Wiley and Sons, New York, 2007.4.Giuseppe Calaore and Laurent El Ghaoui, Optimization Models, Cambridge University Press, 2014.ECE 712 Course Notes by Prof. Jim Reilly, McMaster University, Canada (friendly notes for engineers)http://www.ece.mcmaster.ca/faculty/reilly/ece712/course_notes.htm

Courses

Course Nr. Course name18-pe-2070-vl Matrix Analysis and Computations

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Lecture 3


Course Nr. Course name18-pe-2070-ue Matrix Analysis and Computations

Instructor Type SWSProf. Dr.-Ing. Marius Pesavento Practice 1


Module nameMechatronics Workshop



1 ContentDuring the mechatronic workshop students get the possibility to design and construct their own fixture,which contains a ball track and a ball elevator mechanism. Herefore dimensional plans have to be un-derstood correctly. Afterwards all components (i.e. circuit board, rails and holders) have to be designedand manufactured within the electronic lab and the workshop, where students work independently withturning, drilling and milling machines.The mechatronic workshop allows students to gain practical experience and knowledge in contruction,assembling and PCB layout design.

2 Learning objectives / Learning OutcomesUnderstanding of construction plans, circuit layout design, practical experience with turning, drilling andmilling machines.

3 Recommended prerequisite for participationYou have to bring your own printed copy of the script. This is mandatory for attending the course. Thescript will be published on the moodle platform.





6 Usability of this moduleBSc/MSc ETiT, BSc/MSc MEC


8 References• Lecture Notes „Mechatronics Workshop“• J. Dillinger et al.: Fachkunde Metall, Europa-Lehrmittel, 2007• U. Tietze, C. Schenk, E. Gamm: Halbleiter-Schaltungstechnik, Springer, 2012

Courses

Course Nr. Course name18-bi-1050-pr Mechatronics Workshop



Module nameOptimization of static and dynamic systems


Language Module ownerGerman Prof. Dr. rer. nat. Oskar von Stryk

1 Contentoptimization for static systems:- unconstrained and constrained nonlinear optimization, optimality conditions- numerical Newton type and SQP methods- nonlinear least squares- gradient free optimization methods- practical aspects like problem formulation, approximation of derivatives, method specific parameters, as-sessment of a computed solutionoptimization for dynamic systems:- parameter optimization and estimation problems- optimal control problem- maximum principle and optimality conditions- numerical methods for computing optimal trajectories- optimal feedback control- linear quadratic regulatorapplications and case studies from engineering sciences and roboticstheoretical and practical assignments as well as programming tasks for deepening of knowledge andmethodological skills

2 Learning objectives / Learning OutcomesThrough successful participation students acquire fundamental knowledge and methodological skills inconcepts, techniques and computational methods of optimization for static and dynamic systems and theirapplication for optimization problems in engineering sciences.

3 Recommended prerequisite for participationgrundlegende mathematische Kenntnisse und Fähigkeiten in Linearer Algebra, Analysis mehrerer Verän-derlicher und Grundlagen gewöhnlicher Differentialgleichungen








In dieser Vorlesung findet eine Anrechnung von vorlesungsbegleitenden Leistungen statt, die lt. §25 (2)der 5. Novelle der APB und den vom FB 20 am 30.3.2017 beschlossenen Anrechnungsregeln zu einerNotenverbesserung um bis zu 1.0 führen kann.

8 References- Script of Lecture- J. Nocedal, S.J. Wright: Numerical Optimization, Springer- C.T. Kelley: Iterative Methods for Optimization, SIAM Frontiers in Applied Mathematics- L.M. Rios, N.V. Sahinidis: Derivative-free optimization: a review of algorithms and comparison of softwareimplementations, Journal of Global Optimization (2013) 56:1247-1293- A.E. Bryson, Y.-C. Ho: Applied Optimal Control: Optimization, Estimation and Control, CRC Press- J.T. Betts: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming, SIAMAdvances in Design and Control

Courses

Course Nr. Course name20-00-0186-iv Optimization of static and dynamic systems


6


Module nameLaboratory Matlab/Simulink II

Module Nr. Credit Points Workload Self study Duration Cycle offered18-ko-2070 4 CP 120 h 60 h 1 WiSe/SoSe


1 ContentThe lab is split into the two parts Simulink and Control Engineering II. First the fundamentals of the simu-lation tool Simulink are introduced and their application to problems from different fields of application istrained. In the second part, the knowledge gained in the first part is applied to autonomously solve severalcontrol design problems as well as simulation tasks.

2 Learning objectives / Learning OutcomesThe students will be able to work with the tool MatLab/Simulink on their own and can solve tasks fromthe areas of control engineering and numericial simulation. The students will know the different designmethods of the control system toolbox and the fundamental concepts of the simulation tool Simulink. Theycan practically apply the knowledge gathered in the lectures “System Dynamics and Control Systems I andII” and “Modelling and Simulation”.

3 Recommended prerequisite for participationThe lab should be attended in parallel or after the lectures “System Dynamics and Control Systems II” and“Modelling and Simulation”





6 Usability of this moduleMSc ETiT, MSC MEC


8 ReferencesLecture notes for the lab tutorial can be obtained at the secretariat

Courses

Course Nr. Course name18-ko-2070-pr Laboratory Matlab/Simulink II

Instructor Type SWSProf. Dr.-Ing. Ulrich Konigorski, M.Sc. Marcel Bonnert Internship 4


Module nameLaboratory Control Engineering II



1 ContentDuring the laboratory course the following experiments will be conducted: Coupling control of a helicopter,Non-linear control of a gyroscope, Nonlinear multivariable control of an aircraft, Servo control systems,Control of an overhead crane system, Programmable logic control of a stirring process

2 Learning objectives / Learning OutcomesAfter attending this laboratory course, a student is capable of:

• recalling the basics of the conducted experiments,• organize and comprehend background information for experiments,• assemble experimental set-ups based on manuals,• judge the relevance of experimental results by comparing them with theoretically predicted out-

comes,• present the results of the experiments

3 Recommended prerequisite for participationSystem Dynamics and Control Systems II, the attendance of the additional lecture “System Dynamics andControl Systems III” is recommended


• Module Examination (Study Achievement, Written Examination, Duration: 180 min, Standard Grad-ing System)


• Module Examination (Study Achievement, Written Examination, Weighting: 100 %)

6 Usability of this moduleMSc ETiT, MSc MEC, MSc iST, MSc Wi-ETiT, Biotechnik


8 ReferencesAdamy: Instruction manuals for the experiments (available during the kick-off meeting)

Courses

Course Nr. Course name18-ad-2060-pr Laboratory Control Engineering II

Instructor Type SWSProf. Dr.-Ing. Jürgen Adamy, M.Sc. Jan Christian Zimmermann Internship 4


Module nameAutonomous Driving Lab II


Language Module ownerGerman and English Prof. Dr. rer. nat. Andreas Schürr

1 Content

2 Learning objectives / Learning OutcomesStudents learn to independently develop, implement and present new concepts and algorithms in the fieldof autonomous driving. Realistic problems from the Carolo Cup are solved with existing knowledge andskills practically and the implementation is ensured by quality assurance measures.Students who have successfully participated in this project seminar are able to independently analyze andsolve a complex and realistic task in the field of autonomous driving. The participants acquire the followingskills in detail:

• Further development and optimization of an existing software system and the used algorithms inde-pendently

• Solving and implementation of non-trivial, realistic control engineering challenges• Extensive use of tools for version, configuration, change, and quality assurance management• Realistic time planning and resource allocation (project management)• Further development and optimization of complex hardware/software systems under realistic envi-

ronmental conditions• Planning and implementation of extensive quality assurance measures• Collaboration, communication and organization within the team








8 Referenceshttps://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-ii und Moodle

Courses

Course Nr. Course name18-su-2100-pj Autonomous Driving Lab II

Instructor Type SWSProf. Dr. rer. nat. Andreas Schürr, Dr. Ing. Eric Lenz Project Seminar 3


https://www.es.tu-darmstadt.de/lehre/aktuelle-veranstaltungen/ps-af-ii

Module nameRobust Control



1 Content• Basics (SVD, norms, system representations)• Control design in the frequency domain

– Expressing control tasks as H2 and Hinf optimization problems– Design of H2 and Hinf optimal controllers

• Robust Control– Uncertainity representations (Additive und multiplicative uncertainities, multi model represen-

tations)– Analysis of robustness (Small-Gain-theorem, mu-analysis)– Robust control design in the frequency domainRobust control design by region-based pole

placement

2 Learning objectives / Learning OutcomesThe students are able to express control tasks as H2 and H8 optimization problems, to represent uncer-tainities of a system in a suitable form and to design a controller which ensures robust stability and robustperformance.

3 Recommended prerequisite for participationSystemdynamik und Regelungstechnik I und II







8 References• S. Skogestad, I. Postlethwaite, Multivariable Feedback Control,2. Auflage, 2005, Wiley• K. Zhou, Essentials of Robust Control, 1998, Prentice-Hall• O. Föllinger, Regelungstechnik, 11. Auflage, 2013, VDE Verlag

Courses

Course Nr. Course name18-ko-2140-vl Robust Control

Instructor Type SWSDr. Ing. Eric Lenz Lecture 2


Module nameFundamentals of Reinforcement Learning

Module Nr. Credit Points Workload Self study Duration Cycle offered18-kl-2070 4 CP 120 h 75 h 1 SoSe

Language Module ownerEnglish Prof. Dr.-Ing. Anja Klein

1 Content• Review of Probability Theory• Markov Property and Markov Decision Processes• The Multi-Armed Bandit Problem vs. the Full Reinforcement Learning Problem• Taxonomy of Multi-Armed Bandit Problems (e.g., Stochastic vs. Adversarial Rewards, Contextual

MAB)• Algorithms for Multi-Armed Bandit Problems (e.g., Upper Confidence Interval (UCB), Epsilon-

Greedy, SoftMax, LinUCB) and their Application to Cyber-Physical Networking• Fundamentals of Dynamic Programming and Bellman Equations• Taxonomy of Approaches for the Full Reinforcement Learning Problem (e.g., Temporal-Difference

Learning, Policy Gradient and Actor-Critic)• Algorithms for the Full Reinforcement Learning Problem (e.g., Q-Learning, SARSA, Policy Gradient,

Actor-Critic) and their Application to Cyber-Physical Networking• Linear Function Approximation• Non-linear Function Approximation

2 Learning objectives / Learning OutcomesThe students are able to

• define the Markov property and identify the elements that constitute a Markov decision process. Theywill be able to use these concepts to model decision-making problems in Cyber-Physical Networking.

• determine the characteristics of the Multi-Armed Bandit (MAB) Problem and compare them to thecharacteristics of the Full Reinforcement Learning (RL) Problem.

• determine under which conditions the MAB or the full RL formulation should be used to solvedecision-making problems.

• differentiate the main MAB strategies, e.g., Upper Confidence Interval (UCB), Epsilon-Greedy andSoftmax.

• choose appropriate MAB strategies for the solution of MAB problems.• formulate and solve Contextual-MAB problems.• determine under which conditions Dynamic Programming can be used to solve decision-making

problems.• explain the difference between Dynamic Programming and RL methods.• differentiate between Temporal-Difference, Policy Gradient and Actor-Critic RL techniques.• identify the limitations of MAB and full RL problems.• explain the need for generalization in MAB and full RL problems.• choose appropriate approximation techniques and use them in combination with MAB and full RL

strategies.• apply algorithmic techniques to solve MAB and full RL problems and obtain valid solutions.• judge the reasonableness and consistency of the obtained solutions.

3 Recommended prerequisite for participation• Python or Matlab: basic knowledge• Engineering mathematics and probability theory


• Module Examination (Technical Examination, Written/Oral Examination, Duration: 60 min, Stan-dard Grading System)


The examination takes place in form of a written exam (duration: 60 minutes). If one can estimate thatless than 21 students register, the examination will be an oral examination (duration: 20 min.). The typeof examination will be announced in the beginning of the lecture.


• Module Examination (Technical Examination, Written/Oral Examination, Weighting: 100 %)

6 Usability of this moduleM.Sc. etit: AUT & KTS, M.Sc. ICE, B.Sc. / M.Sc. iST, M.Sc. WI-etit, M.Sc. MEC


8 References• Richard S. Sutton and Andrew G. Barto, “Reinforcement Learning: An Introduction”, A Bradford

Book, Cambridge, MA, USA, 2018.• Aleksandrs Slivkins, “Introduction to Multi-Armed Bandits”, Foundations and Trends in Machine

Learning, Vol. 12: No. 1-2, 2019.

Courses

Course Nr. Course name18-kl-2070-vl Fundamentals of Reinforcement Learning

Instructor Type SWSDr. rer. nat. Sabrina Klos, Dr.-Ing. Andrea Patricia Ortiz Jimenez Lecture 2

Course Nr. Course name18-kl-2070-ue Fundamentals of Reinforcement Learning

Instructor Type SWSDr. rer. nat. Sabrina Klos, Dr.-Ing. Andrea Patricia Ortiz Jimenez Practice 1


m.sc. mechatronics (po 2014)

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