Advances in Industrial Control

Other titles published in this Series:

Digital Controller Implementationand FragilityRobert S.H. Istepanian andJames F. Whidborne (Eds.)

Optimisation of Industrial Processesat Supervisory LevelDoris Sáez, Aldo Cipriano andAndrzej W. Ordys

Robust Control of Diesel Ship PropulsionNikolaos Xiros

Hydraulic Servo-systemsMohieddine Jelali and Andreas Kroll

Strategies for Feedback LinearisationFreddy Garces, Victor M. Becerra,Chandrasekhar Kambhampati andKevin Warwick

Robust Autonomous GuidanceAlberto Isidori, Lorenzo Marconi andAndrea Serrani

Dynamic Modelling of Gas TurbinesGennady G. Kulikov and Haydn A.Thompson (Eds.)

Control of Fuel Cell Power SystemsJay T. Pukrushpan, Anna G. Stefanopoulouand Huei Peng

Fuzzy Logic, Identification and PredictiveControlJairo Espinosa, Joos Vandewalle andVincent Wertz

Optimal Real-time Control of SewerNetworksMagdalene Marinaki and MarkosPapageorgiou

Process Modelling for ControlBenoît Codrons

Computational Intelligence in Time SeriesForecastingAjoy K. Palit and Dobrivoje Popovic

Modelling and Control of mini-FlyingMachinesPedro Castillo, Rogelio Lozano andAlejandro Dzul

Rudder and Fin Ship Roll StabilizationTristan Perez

Hard Disk Drive Servo Systems (2nd Ed.)Ben M. Chen, Tong H. Lee, Kemao Pengand Venkatakrishnan Venkataramanan

Measurement, Control, andCommunication Using IEEE 1588John Eidson

Piezoelectric Transducers for VibrationControl and DampingS.O. Reza Moheimani and Andrew J.Fleming

Manufacturing Systems Control DesignStjepan Bogdan, Frank L. Lewis, ZdenkoKovacic and José Mireles Jr.

Windup in ControlPeter Hippe

Nonlinear H2/H∞ Constrained FeedbackControlMurad Abu-Khalaf, Jie Huang andFrank L. Lewis

Practical Grey-box Process IdentificationTorsten Bohlin

Modern Supervisory and Optimal ControlSandor Markon, Hajime Kita, Hiroshi Kiseand Thomas Bartz-Beielstein

Wind Turbine Control SystemsFernando D. Bianchi, Hernán De Battistaand Ricardo J. Mantz

Practical PID ControlAntonio Visioli

Soft Sensors for Monitoring and Control ofIndustrial ProcessesLuigi Fortuna, Salvatore Graziani,Alessandro Rizzo and Maria GabriellaXibilia

Advanced Control of Industrial ProcessesPiotr TatjewskiPublication due October 2006

Ying Bai, Hanqi Zhuang and Dali Wang (Eds.)

Advanced Fuzzy LogicTechnologies inIndustrial Applications

With 220 Figures

123

Ying Bai, PhDDepartment of Computer Science

and EngineeringJohnson C. Smith UniversityCharlotte, North CarolinaUSA

Dali Wang, PhDDepartment of Physics, Computer Science

and EngineeringChristopher Newport UniversityNewport News, VirginiaUSA

Hanqi Zhuang, PhDDepartment of Electrical EngineeringFlorida Atlantic UniversityBoca Raton, FloridaUSA

British Library Cataloguing in Publication DataAdvanced fuzzy logic technologies in industrial

applications. - (Advances in industrial control)1.Automatic control - Mathematics 2.Fuzzy logicI.Bai, Ying, 1956- II.Zhuang, Hanqi III.Wang, Dali629.8’01511313

ISBN-13: 9781846284687ISBN-10: 1846284686

Library of Congress Control Number: 2006932288

Advances in Industrial Control series ISSN 1430-9491ISBN-10: 1-84628-468-6 e-ISBN 1-84628-469-4 Printed on acid-free paperISBN-13: 978-1-84628-468-7

© Springer-Verlag London Limited 2006

MATLAB® and Simulink® are registered trademarks of The MathWorks, Inc., 3 Apple Hill Drive, Natick,MA 01760-2098, U.S.A. http://www.mathworks.com

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The publisher makes no representation, express or implied, with regard to the accuracy of the informationcontained in this book and cannot accept any legal responsibility or liability for any errors or omissionsthat may be made.

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Advances in Industrial Control

Series Editors

Professor Michael J. Grimble, Professor of Industrial Systems and DirectorProfessor Michael A. Johnson, Professor (Emeritus) of Control Systemsand Deputy Director

Industrial Control CentreDepartment of Electronic and Electrical EngineeringUniversity of StrathclydeGraham Hills Building50 George StreetGlasgow G1 1QEUnited Kingdom

Series Advisory Board

Professor E.F. CamachoEscuela Superior de IngenierosUniversidad de SevillaCamino de los Descobrimientos s/n41092 SevillaSpain

Professor S. EngellLehrstuhl für AnlagensteuerungstechnikFachbereich ChemietechnikUniversität Dortmund44221 DortmundGermany

Professor G. GoodwinDepartment of Electrical and Computer EngineeringThe University of NewcastleCallaghanNSW 2308Australia

Professor T.J. HarrisDepartment of Chemical EngineeringQueen’s UniversityKingston, OntarioK7L 3N6Canada

Professor T.H. LeeDepartment of Electrical EngineeringNational University of Singapore4 Engineering Drive 3Singapore 117576

Professor Emeritus O.P. MalikDepartment of Electrical and Computer EngineeringUniversity of Calgary2500, University Drive, NWCalgaryAlbertaT2N 1N4Canada

Professor K.-F. ManElectronic Engineering DepartmentCity University of Hong KongTat Chee AvenueKowloonHong Kong

Professor G. OlssonDepartment of Industrial Electrical Engineering and AutomationLund Institute of TechnologyBox 118S-221 00 LundSweden

Professor A. RayPennsylvania State UniversityDepartment of Mechanical Engineering0329 Reber BuildingUniversity ParkPA 16802USA

Professor D.E. SeborgChemical Engineering3335 Engineering IIUniversity of California Santa BarbaraSanta BarbaraCA 93106USA

Doctor K.K. TanDepartment of Electrical EngineeringNational University of Singapore4 Engineering Drive 3Singapore 117576

Professor Ikuo YamamotoKyushu University Graduate SchoolMarine Technology Research and Development ProgramMARITEC, Headquarters, JAMSTEC2-15 Natsushima YokosukaKanagawa 237-0061Japan

Series Editors’ Foreword

The series Advances in Industrial Control aims to report and encourage technologytransfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers,actuators, sensors, new industrial processes, computer methods, new applications,new philosophies , new challenges. Much of this development work resides inindustrial reports, feasibility study papers and the reports of advanced collaborativeprojects. The series offers an opportunity for researchers to present an extendedexposition of such new work in all aspects of industrial control for wider and rapiddissemination.

In the mid-1960s and contemporary with Kalman’s pioneering papers on state-space models and optimal control, L.A. Zadeh began publishing papers on “fuzzysets”. It took another decade before the fuzzy-logic controller due to Mamdani and Assilion was reported in the literature (ca. 1974), and now the fuzzy-logic controlparadigm is entering its fifth decade of development and application. Thus, thisnew Advances in Industrial Control monograph edited by Ying Bai, Hanqi Zhuangand Dali Wang on fuzzy-logic control and its practical application comes as atimely reminder of the wide range of problems that can be solved by thiscontinually evolving methodology.

The volume can be considered in two parts, Chapters 1 to 5 cover a range offundamental issues and then Chapters 6 to 21 range across a number of applicationstudies. A cast of authors originating from the US, Mexico, Spain, P.R. China,Estonia, United Kingdom, Taiwan and Chile has contributed these 21 chapters.These diverse contributions have been melded together by editors Ying Bai, HanqiZhuang and Dali Wang to provide a systematic review of recent fuzzy-logiccontrol applications.

The opening group of chapters examines a number of fundamental issues inconstructing and applying fuzzy-logic controllers. The basic constructs of fuzzy-logic namely fuzzy sets, fuzzy rules and the defuzzification procedure are outlinedin Chapter 2. Chapter 3 follows this basic material with a look at how fuzzy-logiccontrollers are implemented. A most interesting distinction between a static fuzzy-logic controller design and a dynamic (or adaptive) fuzzy-logic controller design ismade in these chapters. This is one design route to enhancing the performance of fuzzy-logic controllers. Another route to performance enhancement is to extend the internal architecture of the fuzzy-logic controller by using “fine” and “coarse”

viii Series Editors’ Foreword

lookup tables and this modification is covered in Chapter 2. Also of considerable interest to the industrial engineer will be the material in Chapter 3 relating to the implementation of software and hardware fuzzy-logic controller solutions. Methods to ease the tuning of fuzzy-logic controllers are presented in Chapters 4 and 5. Tuning is a problem because fuzzy-logic controller performance can be tuned using scaled control gains, modifications to the membership functions, changes to the controller rule base or by adjusting entries to the inherent lookup table of the controller. None of these looks particularly simple and Chapters 4 and 5 make contributions to the tuning task.

A particular strength of this new volume in the Advances in Industrial Controlseries is the set of reports from a wide range of application studies, some 16 contributions in total, and these are found in Chapters 6 to 21. The subject areas are as follows: Manufacturing and Business Processes

Laser tracking system ~ Chapter 6 Data mining ~ Chapter 17 Manufacturing robot calibration ~ Chapter 20 Plastic injection moulding ~ Chapter 21

Image Processing Visual image processing ~ Chapter 7 Medical image processing ~ Chapter 8

Vehicle Control Systems Aspects of automobile control ~ Chapters 9, 10 and 11 Mobile robot vehicles ~ Chapter 12 Unmanned aerial vehicle control ~ Chapter 15 and 16

Power System Control Power distribution ~ Chapter 18 Power generation ~ Chapter 19

This is quite a wide range of industrial areas and includes some non-control applications; for example, image processing and the survey chapter on fuzzy-logic applied to data-mining (Chapter 17). An interesting feature of the control problems is that some of them involve the control of “soft” variables like the distance between cars in the context of traffic control or the need for obstacle avoidance in the mobile-robotic-vehicle-control problem. These more relaxed control problems are thought to be more suited to the flexible approaches of fuzzy-logic control. Of the 16 application chapters, one is a survey chapter, eight demonstrate practical results using computer-simulated models and seven involve results from hardware prototypes, hardware experimental laboratory-scale rigs and some field trials. Consequently the volume has a healthy proportion of chapters with results from practical implementations of fuzzy-logic control.

This volume will be of considerable interest to all those involved in the development and application of the fuzzy-logic controller field. Industrial engineers and academic researchers should find the volume a useful indicator of the maturity of the fuzzy-logic controller paradigm and a valuable resource for exploring the potential of these controllers for industrial applications.

M.J. Grimble and M.A. Johnson Glasgow, Scotland, U.K.

Preface

Over the past four decades, the theory of Fuzzy Logic Control (FLC) and itsapplications have been widely developed and implemented in almost all aspects of our modern society, including education and research, manufacturing, medicine,and commercial applications. Application of the FLC technology leads to arevolution in modern control and intelligent control in our time. To help readers tounderstand and apply this technology in the real world, newer, updated informationabout fuzzy logic control and its applications is needed. This book intends to provide readers with advanced materials of the FLC technology applied to today’sindustrial settings. For this purpose, we invited authors, who are among the leadersin the related application fields, to contribute their experience and expertise to thisbook.

The book provides both adequate theoretical background and rich practicalimplementation examples on FLC in industrial applications. Compared with othersimilar books, up-to-date and advanced practical implementation of fuzzy onlogiccontrollers on in industrial applications, such as on-line dynamic control FLC,fuzzy interpolation methods, and various new tuning techniques, are presented.Many of these new techniques have been successfully tested in industrialenvironments, which can be critical for actual implementations, improve greatlyperformance of controllers, and save developers’ time and energy.

The fuzzy inference technology discussed in most of the current books belongsto the category of static fuzzy inference systems, which means that both fuzzy membership functions and control rules are pre-determined prior to theimplementation of a fuzzy logic controller for a plant. This approach is also called 'off-line' fuzzy inference system. The major disadvantage of this technique is thateach output of a lookup table, derived from membership functions and control rules, is calculated based on a pre-assumed input range, which may not be accurateenough to real inputs as this fuzzy inference system is applied to an actual controlsystem. Another drawback of this approach is that it makes a control parametertuning process more difficult because of its 'off-line' nature.

To design a fuzzy logic controller that gives superior performance in a realsystem, the above-mentioned issue needs to be adequately addressed. To this end,a dynamic or 'on-line' fuzzy inference system is introduced and discussed in this

Prefacex

book. In the dynamic fuzzy inference system, membership functions and control rules are not determined until the fuzzy inference system is applied to a physical plant and each output of the lookup table is calculated based on the current inputs to the fuzzy inference system. This possesses the potential of not only greatly improving the control performance of the fuzzy inference system but also facilitating the parameter tuning process.

Another important contribution on the implementation of fuzzy control in the book is that some of new tuning techniques are discussed in a great detail. As everyone knows, the most difficult stage in a fuzzy control application is its tuning phase. How to efficiently tune a fuzzy control system and make it a perfect controller for a real system is a challenging task. Most existing books did not provide much information for such a tuning process. A few of books presented some, but those tuning techniques tend to be out of date and may not improve greatly the control performance of the system involved. New tuning methods, such as the -law technique, the histogram equalization algorithm and the Bezier-based method are discussed in detail in this book. With these tuning techniques, a tuning process can be significantly simplified and the control performance of the system can be greatly improved.

Compared with other similar books on the subject, this book provides more up-to-date and advanced fuzzy control application techniques and examples in different fields, such as laser tracking and control, robot calibration, image processing and pattern recognition, medical engineering, autonomous underwater vehicles, audio systems, and data mining. These application examples will benefit practitioners from various industrial sectors.

The book is written to be easily understood by readers who may have no special knowledge in fuzzy logic and intelligent controls. The targeted readers of this book include a wide variety of people, from design engineers, application engineers and project managers working in industrial controls to undergraduate students, graduate students and faculty members in universities and scientific research laboratories.

Totally twenty chapters are included in this book. Chapter 1 provides basic knowledge of control technologies such as linear control , non-linear control and FLC. The fundamentals of FLC are presented in chapter 2. Chapters 3, 4 and 5 discuss the dynamic or 'on-line' fuzzy control systems and knowledge-based tuning procedures. Beginning in Chapter 6, various fuzzy control applications implemented in different industrial fields are provided. These application fields include:

Laser tracking system Image processing and pattern recognition Medical engineering Guiding of the transportations Automobiles control Autonomous mobile robots control Autonomous underwater vehicles control Fight control Path tracking and obstacles avoidance of UAVs

Preface xi

Data mining control Power networks control Predictive control of a solar power plant Robots calibration Manufacturing welding systems control

Special thanks should be given to all chapter authors and those who have made valuable contributions to this book. This book could not be published without these indispensable contributions.

March 2006

Ying Bai Hanqi Zhuang

Dali Wang

Contents

List of Contributors…………….. .........................................................................xxi

1 From Classical Control to Fuzzy Logic Control ............................................ 1 1.1 Introduction………………........................................................................ 1 1.2 Choice of Controllers................................................................................. 2 1.3 Control Problem …………………............................................................ 2

1.3.1 Motor Speed Control ...................................................................... 21.3.2 Restrictions on Controller Inputs and Outputs ............................... 21.3.3 Nominal Plant................................................................................. 31.3.4 Perturbed Plants.............................................................................. 31.3.5 Performance Criteria....................................................................... 31.3.6 Design Approach ............................................................................ 4

1.4 Analysis and Synthesis of a Robust PID Controller .................................. 41.5 Analysis and Synthesis of a Robust Sliding Mode Controller ................... 51.6 Analysis and Reconstruction of a Fuzzy Robust Controller ...................... 7

1.7 Conclusions…………................................................................................ 81.7.1 Comments on Controller Designs................................................... 81.7.2 From Classical Controls to Fuzzy Logic Controls.......................... 8

Appendix…………….. ....................................................................................... 9 References ………….. ...................................................................................... 15

2 Fundamentals of Fuzzy Logic Control – Fuzzy Sets, Fuzzy Rulesand Defuzzifications ........................................................................................ 17

2.1 Introduction……...................................................................................... 17 2.2 Fuzzy Sets..……...................................................................................... 19

2.2.1 Classical Sets and Operations....................................................... 192.2.2 Mapping of Classical Sets to Functions........................................ 202.2.3 Fuzzy Sets and Operations ........................................................... 222.2.4 Comparison Between Classical and Fuzzy Sets ........................... 24

2.3 Fuzzification and Membership Functions................................................ 24 2.4 Fuzzy Control Rules ................................................................................ 26

2.4.1 Fuzzy Mapping Rules................................................................... 27

Contents xiv

2.4.2 Fuzzy Implication Rules............................................................... 28 2.5 Defuzzification and the Lookup Table..................................................... 29 2.5.1 Mean of Maximum (MOM) Method ............................................ 29 2.5.2 Center of Gravity (COG) Method................................................. 30 2.5.3 The Height Method (HM)............................................................. 30 2.5.4 The Lookup Table ........................................................................ 31 2.5.5 Off-line and On-line Defuzzification............................................ 34 2.6 Architectures of Fuzzy Logic Controls.................................................... 34 2.7 Summary………...................................................................................... 36 References………….. ....................................................................................... 36

3 Implementation of Fuzzy Logic Control Systems ........................................ 37 3.1 Overview………...................................................................................... 37 3.2 Computational Complexity of Fuzzy Logic Control Algorithms………. 38 3.2.1 Fuzzification ................................................................................. 39 3.2.2 Fuzzy Inference ............................................................................ 40 3.2.3 Defuzzification ............................................................................. 42 3.2.4 Static or Dynamic Fuzzy Logic Control ....................................... 43 3.3 Software Solutions................................................................................... 44 3.3.1 General-Purpose Microprocessors................................................ 44 3.3.2 General-Purpose Microprocessors with Fuzzy Support ............... 44 3.3.3 Software Development Tools for Fuzzy Logic System Design.... 45 3.3.4 Online Implementation ................................................................. 46 3.3.5 Lookup Table Implementation ..................................................... 47 3.4 Hardware Solutions.................................................................................. 48 3.4.1 Dedicated Fuzzy Processors ......................................................... 48 3.4.2 Fuzzy ASIC .................................................................................. 50 3.5 Conclusions………….............................................................................. 51 References……………..................................................................................... 51

4 Knowledge-based Tuning I: Design and Tuning of Fuzzy Control Surfaces with Bezier Function ....................................................................................... 53

4.1 Introduction……...................................................................................... 53 4.2 Control Surfaces ...................................................................................... 54 4.3 Tuning with a Genentic Algorithm .......................................................... 59 4.4 Simulation Results ................................................................................... 61 4.5 Conclusions……...................................................................................... 64 References……….…........................................................................................ 64

5 Knowledge-based Tuning II: -Law Tuning of a Fuzzy Lookup Table..... 67 5.1 Introduction……...................................................................................... 67 5.2 Gain and Surface Tuning of a Fuzzy Lookup Table ................................ 67 5.2.1 PD Controller and Fuzzy PD Controller....................................... 68 5.2.2 -Law Tuning of Fuzzy Lookup Table ........................................ 69 5.3 Simulation and Experimental Results ...................................................... 74 5.4 Summary………...................................................................................... 80 References……….…........................................................................................ 80

Contents xv

6 Apply a Fuzzy Logic Controller to Suppress Noises and Coupling Effects for a Laser Tracking System…...................................................................... 83

6.1 Introduction……...................................................................................... 83 6.2 Operating Principle of LTS...................................................................... 85 6.3 The Control System, Coupling Effects and Nonlinearity ........................ 87 6.4 Design of Fuzzy Logic Controller ........................................................... 90 6.5 Simulation and Experimental Results ...................................................... 93 6.6 Conclusion…… ....................................................................................... 98 Acknowledgement…… .................................................................................... 98 References.………..…...................................................................................... 98

7 Fuzzy Logic for Image Processing: Definition and Applications of a Fuzzy Image Processing Scheme .......................................................... 101

7.1 Introduction…….................................................................................... 101 7.2 Fuzzy Image Processing Scheme Definition.......................................... 102 7.2.1 Mapping the Image into the Fuzzify Domain............................. 102 7.2.2 Operation on the Fuzzy Domain................................................. 104 7.2.3 Defuzzification Function ............................................................ 105 7.3 Examples of Fuzzy Image Processing .................................................. 106 7.3.1 Fuzzy Binarization...................................................................... 106 7.3.2 Fuzzy Edge Definition................................................................ 108 7.3.3 Fuzzy Geometry Measurement................................................... 110 7.4 Results and Conclusions ........................................................................ 111 Acknowledgement……................................................................................... 112 References………….. ..................................................................................... 112

8 Fuzzy Logic for Medical Engineering: An Application to Vessel Segmentation ………………………………………………………………..115

8.1 Introduction…….................................................................................... 115 8.2 Two Retinal Vessel Detection Procedures............................................. 117 8.3 Different Averages of a Fuzzy Set......................................................... 117 8.4 The Heneghan Method with the Vorob’ev Fuzzy Set Average ............. 122 8.5 The Zana-Klein Method and the Fuzzy Set Averages ........................... 123 8.6 Some Conclusions and Further Developments ...................................... 124 Acknowledgement……................................................................................... 127 References……….…...................................................................................... 127

9 Fuzzy Logic for Transportation Guidance: Developing Fuzzy Controllers for Maintaining an Inter-Vehicle Safety Headway .................................... 129

9.1 Introduction…….................................................................................... 129 9.2 The Autopia Program............................................................................. 130

9.2.1 Application of a Fuzzy Coprocessor to Automatic Vehicle Driving .......................................................................... 131

9.2.2 The Longitudinal Control System .............................................. 132 9.2.2.1 Design of the Fuzzy Cruise Control System ................ 133 9.2.2.2 Fuzzy Adaptive Cruise Control Extension................... 135

Contents xvi

9.3 Experiment Results ................................................................................ 138 9.4 Conclusion……. .................................................................................... 141 Acknowledgement……................................................................................... 141 References …………... ................................................................................... 141

10 Fuzzy Logic Control for Automobiles I: Knowledge-based Gear-position Decision…………………………. ................................................................. 145

10.1 Introduction…….................................................................................... 145 10.2 Knowledge-based GPD (KGPD) ........................................................... 146 10.2.1 Basic Ideas.................................................................................. 146 10.2.2 Parameter Measurement and Normalization............................... 149 10.2.3 Road Environment and Driver’s Intention Recognition ............. 150 10.2.4 Shift Schedules ........................................................................... 152 10.2.5 Gear-Position Calculation........................................................... 152 10.3 Experiment Test..................................................................................... 153 10.4 Conclusion…… ..................................................................................... 155 References..……….….................................................................................... 156

11 Fuzzy Logic Control for Automobiles II: Navigation and Collision Avoidance System……………………………… .................. 159 11.1 Introduction…….................................................................................... 159 11.2 Problem Formulation ............................................................................. 160 11.3 Vehicle Navigation Control ................................................................... 161 11.4 Truck and Trailer Backing Control........................................................ 164 11.5 Collision Avoidance System.................................................................. 165 11.5.1 State Transition Control.............................................................. 167 11.6 Results and Discussion .......................................................................... 169 11.7 Conclusions…….................................................................................... 172 Acknowledgement……................................................................................... 172 References…………... .................................................................................... 173

12 Fuzzy Logic Based Control Mechanisms for Handling the Uncertainties Facing Mobile Robots in Changing Unstructured Environments ............ 175

12.1 Introduction…….................................................................................... 175 12.2 Type-2 Fuzzy Sets ................................................................................. 177 12.2.1 Interval Type-2 Fuzzy Sets......................................................... 179 12.2.2 Type-2 Set Theoretic Operations ............................................... 180 12.3 Interval Type-2 Fuzzy Logic Controller (FLC) ..................................... 181 12.3.1 The Fuzzifier .............................................................................. 182 12.3.2 Rule Base ................................................................................... 182 12.3.3 Fuzzy Inference Engine .............................................................. 182 12.3.4 Type-Reduction .......................................................................... 183 12.3.5 Defuzzification ........................................................................... 184 12.4 Experiments and Results........................................................................ 185 12.5 Conclusions…….................................................................................... 188 References………….….................................................................................. 189

Contents xvii

13 Combine Sliding Mode Control and Fuzzy Logic Control for Autonomous Underwater Vehicles…………… ................................... 191

13.1 Introduction…….................................................................................... 191 13.2 Autonomous Underwater Vehicles (AUVs) ......................................... 193 13.3 Sliding Mode Controller ........................................................................ 194 13.4 Fuzzy Sliding Mode Control (FSMC) ................................................... 196 13.5 Sliding Mode Fuzzy Control (SMFC) ................................................... 198 13.6 Sliding Mode Fuzzy Control Applied to AUVs..................................... 201 13.7 Conclusions…….................................................................................... 203 References…………..…................................................................................. 203

14 Fuzzy Logic for Flight Control I: Nonlinear Optimal Control of Helicopter Using Fuzzy Gain Scheduling .............................................. 207

14.1 Introduction…….................................................................................... 207 14.2 The Nonlinear Dynamics of the Helicopter ........................................... 208 14.3 Design of Linear Optimal Controller ..................................................... 211 14.4 Gain Scheduling Using Takagi-Sugeno Fuzzy Model........................... 214 14.5 Conclusions…….................................................................................... 220 Acknowledgement……................................................................................... 220 References……………................................................................................... 220

15 Fuzzy Logic for Flight Control II: Fuzzy Logic Approach to Path Tracking and Obstacles Avoidance of UAVs.............................................. 223 15.1 Introduction…….................................................................................... 223 15.2 Strategy for Path Tracking and Obstacle Avoidance ............................. 224 15.3 Simulation Results ................................................................................. 231 15.4 Conclusions…….................................................................................... 231 Acknowledgement……................................................................................... 234 References……………................................................................................... 234

16 Close Formation Flight Control of Multi-UAVs via Fuzzy Logic Technique…. ..................................................................... 237 16.1 Introduction…….................................................................................... 237 16.2 Flight Model……………………………………………………………238 16.3 Fuzzy Logic Control .............................................................................. 240 16.4 Simulations……. ................................................................................... 244 16.5 Conclusion……. .................................................................................... 246

Acknowledgement….. .................................................................................... 247 References……………................................................................................... 247

17 Applications of Fuzzy Logic in Data Mining Process ................................ 249 17.1 Introduction…….................................................................................... 249 17.2 Foundation of Data Mining.................................................................... 250 17.3 Fuzzy Logic for Data Mining Basic....................................................... 254 17.4 Algorithms of Fuzzy Logic in Data Mining Process.............................. 255 17.5 Conclusion…… ..................................................................................... 259

Contents xviii

References…………..…................................................................................. 259 18 Fuzzy Logic Control for Power Networks: A Multilayer

Fuzzy Controller ……................................................................................... 261 18.1 Introduction…….................................................................................... 261 18.2 Description of Fuzzy Logic Systems ..................................................... 262 18.3 Functional Design of the Proposed Controller....................................... 263 18.4 Overall System Structure ....................................................................... 268 18.5 Implementation and Test Results ........................................................... 269 18.5.1 The WSCC Test System............................................................. 270 18.5.2 Four-generator Test System........................................................ 273 18.6 Conclusion……. .................................................................................... 276 References…………….. ................................................................................. 277

19 Fuzzy Predictive Control for Power Plants ................................................ 279 19.1 Introduction…….................................................................................... 279 19.2 Model Predictive Control....................................................................... 280 19.3 Model Predictive Control for Power Plants ........................................... 283 19.4 Fuzzy Control of Power Plants .............................................................. 284 19.5 Fuzzy Systems and Model Predictive Control ....................................... 286

19.6 Integrating Fuzzy Systems and Model Prediction for Power Plant Control ......................................................................... 287

19.7 Supervisory Fuzzy Predictive Control of a Boiler ................................. 289 19.8 Predictive Control of a Solar Power Plant with Fuzzy Goals and Constraints…….. ............................................................................ 291

19.9 Conclusions…….................................................................................... 294 Acknowledgement………............................................................................... 295 References……………................................................................................... 295

20 Fuzzy Logic for Robots Calibration – Using Fuzzy Interpolation Technique in Modeless Robot Calibration.................................................. 299 20.1 Introduction…….................................................................................... 299 20.2 On-line 3D Fuzzy Interpolation ............................................................. 300 20.3 MATLAB Simulation Results ............................................................... 306

20.4 The Comparison Between the Modeless and Model-based Robots Calibration Methods .................................................................. 309

20.5 Conclusions…….................................................................................... 313 References…………….. ................................................................................. 313 21 Fuzzy Control on Manufacturing Welding Systems: To Apply Fuzzy

Theory in the Control of Weld Line of Plastic Injection-molding ............ 315 21.1 Introduction…….................................................................................... 315 21.2 Fuzzy Control Logic for the Weld Line Position................................... 316 21.2.1 Fuzzification Interface................................................................ 316 21.2.2 Production Rule .......................................................................... 316 21.2.3 Decision Making Logic .............................................................. 317 21.2.4 Defuzzification Interface ............................................................ 318

Contents xix

21.3 Case Analyses…….. .............................................................................. 318 21.4 Simulation and Experimental Results ................................................... 321 21.5 Conclusion…… ..................................................................................... 323 Acknowledgement……................................................................................... 323 References ………….. .................................................................................... 324

Index…………….………… ............................................................................... 325

List of Contributors

Chapter 1Charles P. Coleman Dept of Aeronautics and Astronautics

Massachusetts Institute of TechnologyCambridge, MA 02139, USAe-mail: ccoleman@mit.edu

Chapters 2, 3 and 20Ying Bai Dept of Computer Science and Engineering

Johnson C. Smith UniversityCharlotte, NC 28216, USAe-mail: ybai@jcsu.edu

Dali Wang Dept of Physics, ComputerScience & Engineering

Christopher Newport University Newport News, VA 23606, USA e-mail: dwang@pcs.cnu.edu

Chapter 4Hanqi Zhuang Department of Electrical Engineering

Florida Atlantic UniversityBoca Raton, FL 33431e-mail: zhuang@fau.edu

S. Wongsoontorn Department of Electrical EngineeringFlorida Atlantic UniversityBoca Raton, FL 33431

Chapter 5Hanqi ZhuangYingli Wang Software Engineer

3874 Corina WayPalo Alto, CA 94303e-mail: victorw@gmail.com

List of Contributorsxxii

Chapter 6 Ying Bai Hanqi Zhuang Zvi S. Roth Department of Electrical Engineering

Florida Atlantic University Boca Raton, FL 33431 e-mail: zroth@fau.edu

Chapter 7 Mario I. Chacón-Murguía Graduate and Research Department

Chihuahua Institute of Technology Chihuahua Chih., CP 31310 Mexico e-mail: mchacon@itchihuahua.edu.mx

Chapter 8 Guillermo Ayala Dept. de Estadística e Investigación Teresa León Operativa, Universidad de Valencia Victoria Zapater Avda. Vicent Andrés Estellés 1

46100-Burjasot, Spain email: Guillermo.Ayala@uv.es

email: Teresa.Leon@uv.es email: zapater_mvi@gva.es

Chapter 9 José E. Naranjo Instituto de Automática Industrial (CSIC) Carlos González Ctra. Campo Real Km. 0,200 Ricardo García La Poveda-Arganda del Rey Teresa de Pedro 28500, Madrid, Spain

email: jnaranjo@iai.csic.esemail: gonzalez@iai.csic.esemail: ricardo@iai.csic.esemail: tere@iai.csic.es

Chapter 10 Guihe Qin Department of Computer Science and

Technology Jilin University ChangChun 130025, P.R.China e-mail: ghqin@jlu.edu.cn

Anlin Ge College of Automobile Engineering Jilin UniversityChangChun 130025, P.R.China e-mail: geal@jlu.edu.cn

Ju-Jang Lee Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Deajeon 305-701, Korea

List of Contributors xxiii

Chapter 11 Andri Riid Department of Computer ControlDmitri Pahhomov Tallinn University of Technology Ennu Rüstern Ehitajate tee 5, Tallinn, 19086, Estonia

email: andri@dcc.ttu.eeemail: dmipah@hot.eee-mail: ennu.rystern@dcc.ttu.ee

Chapter 12 Hani Hagras Department of Computer Science

The University of Essex Wivenhoe Park Colchester CO4 3SQ, England, UK e-mail: hani@essex.ac.uk

Chapter 13 Feijun Song Department of Ocean Engineering

Florida Atlantic University 101 N. Beach Road Dania, FL 33004, USA e-mail: fsong@oe.fau.edu

Samuel M. Smith Adpet Systems, Inc. 2966 Fort Hill Road Eagle Mountain, UT 84043, USA e-mail: smithsm@samuelsmith.org

Chapter 14 Gwo-Ruey Yu Department of Electrical Engineering

National Ilan University 1, Sec. 1, Shen-Lung Road Ilan City, Taiwan 260 e-mail: gwoyu@niu.edu.tw

Chapter 15 Zhao Sun National Institute of Aerospace Ran Zhang 100 Exploration Way

Hampton, VA 23666, USA e-mail: zhaosun@nianet.orge-mail: ranzhang@nianet.org

Tao Dong Electrical and Computer Engineering Xiaohong Liao North Carolina A&T State University David Y. Song 1601 E. Market Street

Greensboro, NC 27411, USA e-mail: tdong@ncat.edue-mail: xliao@ncat.edue-mail: songyd@ncat.edu

List of Contributorsxxiv

Chapter 16 Yao Li Institute for System Research

Department of Electrical and Computer Engineering University of Maryland College Park, MD 20742e-mail: lee_yao2003@yahoo.com

Zhao Sun National Institute of Aerospace Ran Zhang 100 Exploration Way

Hampton, VA 23666, USA e-mail: zhaosun@nianet.orge-mail: ranzhang@nianet.org

Bin Li Electrical and Computer Engineering Liguo Weng North Carolina A&T State University David Y. Song 1601 E. Market Street

Greensboro, NC 27411, USA e-mail: libinncat@yahoo.come-mail: liguo_weng@hotmail.come-mail: songyd@ncat.edu

Chapter 17 Hang Chen Department of Computer Science and

Engineering Johnson C. Smith University 100 Beatties Ford Rd Charlotte, NC 28216 USA e-mail: hchen@jcsu.edu

Chapter 18 Ahmed Rubaai Department of Electrical and Computer Abdul R. Ofoli Engineering

Howard University Washington DC 20059, USA

e-mail: arubaai@howard.edue-mail: ofoli@yahoo.com

Chapter 19 Aldo Z. Cipriano Department of Electrical Engineering

Pontificia Universidad Católica de Chile Vicuña Mackenna 4860, P. O. Box 306 Santiago 6904411, Chile e-mail: aciprian@ing.puc.cl

List of Contributors xxv

Chapter 21 Mei-Yung Chen Department of Industrial Education

National Taiwan Normal University Taipei, 106, Taiwan, R. O. C. email: cmy@ntnu.edu.tw

Yi-Cheng Chen Institute of Sports Equipment Technology Taipei Physical Education CollegeTaipei, 111, Taiwan, R. O. C. email: ycchen@tpec.edu.tw

Shia-Chung Chen Dept of Mechanical Engineering Chung Yuan Christian University Chung-Li, 320, Taiwan, R. O. C. email: shiachun@cycu.edu.tw