mechatronik systems guderjahn

LLP Erasmus Intensive Programme LLP Erasmus Intensive Programme

Partner: Cologne University of Applied Sciences

Turku, Finland, 14 - 27 March, 2010

IP 2010 - Ecology and Safety as Driving Forces in the Development of Vehicles IP 2010 - Ecology and Safety as Driving Forces in the Development of Vehicles

An Introduction to Automotive

Mechatronic Systems

Dipl.-Ing. Jan Guderjahn

Cologne University of Applied Sciences

Cologne Laboratory of Mechatronics





Overview

• What is Mechatronic?

• Components of a Mechatronic System

• Mechatronical Development Cycle

• Mechatronic System Applications

Contents





Closer Looks

• Mechatronical Development Cycle - Modeling

• Mechatronical Development Cycle - Analysis and Synthesis

• Rapid Control Prototyping

Conclusion

• Summary

• Examples

• Discussion

Contents (cont’d)





Introduction

Jan Guderjahn (contact: [email protected])

• Graduated in Automotive Engineering at Cologne University of Applied Sciences in January 2007

• Work at the Cologne Laboratory of Mechatronics (CLM)

• Vehicle dynamics simulation, HIL simulation, control system design and drive systems

• Graduating in Master of Science Mechatronics in 2010

This lecture is based upon the lecture „Mechatronic Systems“ of Prof. Dr.-Ing. Henrichfreise, head of the Cologne Laboratory of Mechatronics.





Overview

Closer Looks

Conclusion

Contents





What is Mechatronic?

Mechatronic – An Integrative Engineering Science

Development of optimal technical (mechatronical) systems





What is Mechatronic? (cont'd)

Contributions of participating disciplines

• Mechanical Engineering: Construction of mechanism

• Electronical Engineering: Actuators, sensors, signal/power processing

• Simulation Technology: Numeric simulation

• Control Engineering: System analysis and synthesis

• Informatics: Control system implementation





Aims and Characteristics

• Optimal system behavior for specified objectives

• Benefit from synergy effect

• Tool-based development environment

• Reduced overall development time and costs

What is Mechatronic? (cont'd)





Components of a Mechatronic System

Signal Processing

Power Processing





Mechatronical Development Cylce





Mechatronic System Applications

Control System Design

• Feedback of real process/plant is input for real-time CPU

• Real-time CPU computes control algorithms

• Output of real-time CPU is input for real process/plant

• Objective: Optimal control system design





Mechatronic System Applications (cont’d)

Hardware-in-the-Loop (HIL) Simulation

• Real production hardware and software, e.g. automotive production Electronic Control Unit (ECU)

• Real-time CPU computes simulation model (e.g. vehicle model)

• Objectives: Optimization of control parameters and simulation model





Mechatronic System Applications (cont’d)

Virtual Control Prototyping

• Hybrid form of control system design and HIL simulation

• Real-time simulation and animation of test system

• Implementation of control algorithms on 2nd real-time processor

• Coupling of both real-time systems

• Objective: Simultaneously optimization of mechanism and control system design





Overview

Closer Looks

Conclusion

Contents





Mechatronical Development Cycle - Modeling

Modeling Stages

1. Real system or system idea

2. Physical analogue model

3. Free body diagram for mechanical parts

4. Laws of nature and applied sciences, components laws and phenomenons (*)

5. Mathematical model

* Spring, damper, weight, Newton's Laws of Motion, Euler's Equations, resistance, capacitor, inductance,

Kirchhoff's Circuit Laws, Ampère's Circuital Law, Bernoulli's Principle , Continuity Equation





Components of Mechatronic Systems - Mechanisms

Mass-spring-damper-system

Physical analogue system: Laws:

Free body diagram: Mathematical model:






2-mass-spring-damper-system








Leaver mechanism







Gear box






Components of Mechatronic Systems - Actuators

…

DC motor







Hydraulic drive


Mathematical model:


…






Active magnetic bearing


Mathematical model:





Components of Mechatronic Systems - Sensors

Light bulb

(LED)

Disc Blind

Photo

transistor

Logic

gate

Count

Position

Incremental encoder

• Position measurement via count of single events, e.g. electric impulses

• Signal processing unit

• Signal edge evaluation

• Identification of movement direction





Components of Mechatronic Systems - Sensors

Tachometer

• Measurement of angular velocity

• Velocity proportional output voltage at the collector connectors

• Ripple, offset, noise and gain deviation

• Compensation of offset and gain deviation via signal conditioning

• Consideration of ripple and noise during control system design

Motor

bearing 1

Motor

bearing 2

Rotor of

Motor

Rotor of

DC Tacho

Nobel metal

collector

Nobel metal

brushes





Interplay of Components of Mechatronic Systems

Electronic Power Steering (EPS) System

Real system Physical analogue system:

5. VDI Mechatronik Tagung 2003, Innovative Produktentwicklung. Fulda, 07.-08. Mai 2003. Optimale Regelung einer elektromechanischen Servolenkung Hermann Henrichfreise, Jürgen Jusseit Labor für Mechatronik (CLM), Fachhochschule Köln, www.clm-online.de Harwin Niessen Mercedes-Benz Lenkungen GmbH (MBL), Entwicklungszentrum Esslingen

+ actuators and sensors





Mechatronical Development Cycle - Analysis and Synthesis

Ways to describe mechatronic systems

• Ordinary Differential Equations (ODE)

• Frequency response

• Transfer functions

• Block diagrams

• State space systems





Simplest way to describe mechatronic systems

• Solution of homogenous part with and u(t) = 0 leads to the characteristic polynomial

• Location of roots of the characteristical polynomial allow statements on system stability

• Computation of system responses for various system excitations u(t) via particular solution

Linear Ordinary Differential Equations (ODE)





Linear Ordinary Differential Equations (ODE) (cont'd)

Example:

Characteristic polynomial

Roots of characteristic polynomial





Linear Ordinary Differential Equations (ODE) (cont'd)

Partial solution for various system responses

• Step

• Ramp

• Harmonic oscillation

Frequency response

time / sec

time / sec

time / sec





Phase / deg

Frequency Responses

Relation between system excitation and system amplitude and phase response

• Only harmonic oscillation as excitation and system response

• Only defined for stable systems

• Only linear time-invariant systems

• Easy to plot and analyze via Bode and Nyquist diagram

omega / rad/sec

Phase / d

eg

Am

p / d

B





Transfer Functions

Relation between any system excitation and system response

• Laplace transform maps signals from time-domain f(t) (original) to s-domain F(s) (image)

• Only single-input-single-output (SISO) systems

• Only linear time-invariant systems

Block diagrams





Block Diagrams

Numeric simulation with MATLAB/Simulink





State Space Systems

Overview

• Technical systems are often described by a system of coupled ODEs nth order for multiple inputs and ouputs

• Suitable transforms lead to a system of coupled 1st order ODE

General time-invariant linear state space system: A state matrix x state vector B input matrix y output vector C output matrix u input vector D feedthrough matrix

state differential equation and

output equation





State Space Systems (cont’d)

Key benefits

• Linear, non-linear, time-variant and time-invariant systems with any excitation

• Multiple-input-multiple-output (MIMO) systems

• Most numeric simulations are based upon state space systems (e.g. MATLAB/Simulink)

• Many newer control theories are based upon state space systems (especially for MIMO systems)

• Classical control theories become easier to use (e.g. for SISO systems)





State Space Systems (cont’d)

state differential equations output equation

Example: � Transform to system of coupled 1st order ODE

and leads to

In matrix-vector form:

Eigen values of state matrix A are the roots of the characteristic polynomial





Interplay of Components of Mechatronic Systems

Electronic Power Steering (EPS) System

5. VDI Mechatronik Tagung 2003, Innovative Produktentwicklung. Fulda, 07.-08. Mai 2003. Optimale Regelung einer elektromechanischen Servolenkung Hermann Henrichfreise, Jürgen Jusseit Labor für Mechatronik (CLM), Fachhochschule Köln, www.clm-online.de Harwin Niessen Mercedes-Benz Lenkungen GmbH (MBL), Entwicklungszentrum Esslingen





Rapid Control Prototyping

Overview

• Seamless tool chain as development environment for mechatronic systems

• MATLAB with toolboxes

• dSPACE real-time software and hardware

• Focus only on development objectives

• System validation and optimal prototype

• Specificate requirements for production hardware

• Reduction of development time and costs





Rapid Control Prototyping – Steps and Tools

1. Modeling and analysis, reveal system deficiencies, specification of control design objectives

2. Control system synthesis, design of control system structure, computation of controller parameters

3. Analysis of non-linear closed-loop system via numeric simulation

4. Controller separation, augmentation of controller model, with real-time hardware interface blocks

MATLAB/Control System Toolbox

MATLAB/Simulink, dSPACE real-time interface block library

MATLAB/Simulink

MATLAB/Simulink and Control System Toolbox





Rapid Control Prototyping – Steps and Tools (cont’d)

5. Controller implementation on prototyping hardware via automatic code generation

6. Real system setup, initialization of hardware and safety environment, visualization of signals for monitoring and control

7. Analysis of real system via experiments, comparison of measurement and simulation results Steps 4 to 6: „Rapid Control Prototyping“

dSPACE ControlDesk and MLIB

dSPACE Control Desk and MLIB MATLAB/Simulink

MATLAB/Simulink and real-time workshop, dSPACE real-time interface and prototyping hardware





Overview

Closer Looks

Conclusion

Contents





Summary

The presented formal design process, defined by the mechatronical development cycle, leads during the completely tool-based development of mechatronic systems to optimal results and reduces in comparison to traditional methods the overall development time and costs.

This way the interdisciplinary character, the basic idea of optimization and the formal development method establish potentials for the products of the future.





Activities

• Development of vehicle models

• Control algorithm design for steering systems

• Vehicle dynamics control

• Vehicle dynamics observation

• HIL simulation

Our work at the CLM - Automotive Mechatronic Systems

Models and methods for the development and test of mechatronic autmotive systems





Development of Vehicle Models

Activities

• Development of specific vehicle multi-body systems

• Development of vehicle component models (suspension, power train, tires, brakes etc.)

• Development of models for roads and drivers

Real-time models for vehicle dynamics simulation





steering

wheel

steering

column

sensor

gear

EPS motor rack

and pinion

steering lever

and steering rod wheels

EPS-ECU

Control Algorithm Design For Steering Systems

Activities

• Electro-mechanical and hydraulical steering systems

• Superimposed steering systems

• Design of steering-feel adapted to current vehicle dynamics situation

Control concepts for safe and comfortable driving





Vehicle Dynamics Control

Activities

• Steering intervention via superimposition torques

• Driver assistance in critical vehicle dynamics situations

• Vehicle stabilizing via steering actuation

• Steering feel optimization

Control concepts for safe and comfortable driving





0 1 2 3 4 5 6 7 8 9 10

-6

-4

-2

0

2

4

6

time / s

sum

late

ral tire

forc

es fro

nt / kN

Vehicle Dynamics Observation

Activities

• Development of observer for the estimation of vehicle dynamics variables

• Use of signal models instead of tire models

• Estimation of traction potential of tire contact patches

Observer

Reliable estimations independent of road and tire conditions





HIL Simulation

HIL test bench for steering systems

• Haptic feedback via steering wheel

• Development of algorithms considering the steering feel

• Front-loading of tests with prototypes to the HIL simulation

Development of algorithms in regard to the driver‘s acceptance of the steering feel

ideal

steering wheel





Drive systems

• Rapid Control Prototyping

• Development of software tools

Aviation

• Estimation of gusts and structural loads for commercial aircrafts

• Active wing vibration damping

Robotics

• Lectures for educational purposes

Our work at the CLM – Other Activities





Discussion

Thank you for your attention.

An Introduction to Automotive

Mechatronic Systems

Dipl.-Ing. Jan Guderjahn

Cologne University of Applied Sciences

Cologne Laboratory of Mechatronics

www.clm-online.de

mechatronik systems guderjahn

Documents

benz lenkungen

phase deg

free body

rapid control

state space

time sec

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electronic