John H Lilly

Fuzzy Control and Identification (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 248
• Erscheinungstermin: 18. Oktober 2010
• Englisch
• ISBN-13: 9780470872710
• Artikelnr.: 37299836

Autorenporträt

John H. Lilly, PhD, is a professor in the Speed School of Engineering at the University of Louisville. His research interests are nonlinear and adaptive control, fuzzy identification and control, positive/negative fuzzy systems, pneumatic muscle actuators, and robotics. In addition to his twenty-eight years of teaching experience, Dr. Lilly has written more than fifty refereed journal and conference articles, book chapters, invited scholarly lectures, and seminars.

Inhaltsangabe

PREFACE. CHAPTER 1 INTRODUCTION. 1.1 Fuzzy Systems. 1.2 Expert Knowledge.
1.3 When and When Not to Use Fuzzy Control. 1.4 Control. 1.5
Interconnection of Several Subsystems. 1.6 Identification and Adaptive
Control. 1.7 Summary. Exercises. CHAPTER 2 BASIC CONCEPTS OF FUZZY SETS.
2.1 Fuzzy Sets. 2.2 Useful Concepts for Fuzzy Sets. 2.3 Some Set Theoretic
and Logical Operations on Fuzzy Sets. 2.4 Example. 2.5 Singleton Fuzzy
Sets. 2.6 Summary. Exercises. CHAPTER 3 MAMDANI FUZZY SYSTEMS. 3.1 If-Then
Rules and Rule Base. 3.2 Fuzzy Systems. 3.3 Fuzzification. 3.4 Inference.
3.5 Defuzzification. 3.5.1 Center of Gravity (COG) Defuzzification. 3.5.2
Center Average (CA) Defuzzification. 3.6 Example: Fuzzy System for Wind
Chill. 3.6.1 Wind Chill Calculation, Minimum T-Norm, COG Defuzzification.
3.6.2 Wind Chill Calculation, Minimum T-Norm, CA Defuzzification. 3.6.3
Wind Chill Calculation, Product T-Norm, COG Defuzzification. 3.6.4 Wind
Chill Calculation, Product T-Norm, CA Defuzzification. 3.6.5 Wind Chill
Calculation, Singleton Output Fuzzy Sets, Product T-Norm, CA
Defuzzification. 3.7 Summary. Exercises. CHAPTER 4 FUZZY CONTROL WITH
MAMDANI SYSTEMS. 4.1 Tracking Control with a Mamdani Fuzzy Cascade
Compensator. 4.1.1 Initial Fuzzy Compensator Design: Ball and Beam Plant.
4.1.2 Rule Base Determination: Ball and Beam Plant. 4.1.3 Inference: Ball
and Beam Plant. 4.1.4 Defuzzification: Ball and Beam Plant. 4.2 Tuning for
Improved Performance by Adjusting Scaling Gains. 4.3 Effect of Input
Membership Function Shapes. 4.4 Conversion of PID Controllers into Fuzzy
Controllers. 4.4.1 Redesign for Increased Robustness. 4.5 Incremental Fuzzy
Control. 4.6 Summary. Exercises. CHAPTER 5 MODELING AND CONTROL METHODS
USEFUL FOR FUZZY CONTROL. 5.1 Continuous-Time Model Forms. 5.1.1 Nonlinear
Time-Invariant Continuous-Time State-Space Models. 5.1.2 Linear
Time-Invariant Continuous-Time State-Space Models. 5.2 Model Forms for
Discrete-Time Systems. 5.2.1 Input-Output Difference Equation Model for
Linear Discrete-Time Systems. 5.2.2 Linear Time-Invariant Discrete-Time
State-Space Models. 5.3 Some Conventional Control Methods Useful in Fuzzy
Control. 5.3.1 Pole Placement Control. 5.3.2 Tracking Control. 5.3.3 Model
Reference Control. 5.3.4 Feedback Linearization. 5.4 Summary. Exercises.
CHAPTER 6 TAKAGI-SUGENO FUZZY SYSTEMS. 6.1 Takagi-Sugeno Fuzzy Systems as
Interpolators between Memoryless Functions. 6.2 Takagi-Sugeno Fuzzy Systems
as Interpolators between Continuous-Time Linear State-Space Dynamic
Systems. 6.3 Takagi-Sugeno Fuzzy Systems as Interpolators between
Discrete-Time Linear State-Space Dynamic Systems. 6.4 Takagi-Sugeno Fuzzy
Systems as Interpolators between Discrete-Time Dynamic Systems described by
Input-Output Difference Equations. 6.5 Summary. Exercises. CHAPTER 7
PARALLEL DISTRIBUTED CONTROL WITH TAKAGI-SUGENO FUZZY SYSTEMS. 7.1
Continuous-Time Systems. 7.2 Discrete-Time Systems. 7.3 Parallel
Distributed Tracking Control. 7.4 Parallel Distributed Model Reference
Control. 7.5 Summary. Exercises. CHAPTER 8 ESTIMATION OF STATIC NONLINEAR
FUNCTIONS FROM DATA. 8.1 Least-Squares Estimation. 8.1.1 Batch Least
Squares. 8.1.2 Recursive Least Squares. 8.2 Batch Least-Squares Fuzzy
Estimation in Mamdani Form. 8.3 Recursive Least-Squares Fuzzy Estimation in
Mamdani Form. 8.4 Least-Squares Fuzzy Estimation in Takagi-Sugeno Form. 8.5
Gradient Fuzzy Estimation in Mamdani Form. 8.6 Gradient Fuzzy Estimation in
Takagi-Sugeno Form. 8.7 Summary. Exercises. CHAPTER 9 MODELING OF DYNAMIC
PLANTS AS FUZZY SYSTEMS. 9.1 Modeling Known Plants as Takagi-Sugeno Fuzzy
Systems. 9.2 Identification in Input-Output Difference Equation Form. 9.2.1
Batch Least-Squares Identification in Difference Equation Form. 9.2.2
Recursive Least-Squares Identification in Input-Output Difference Equation
Form. 9.2.3 Gradient Identification in Input-Output Difference Equation
Form. 9.3 Identification in Companion Form. 9.3.1 Least-Squares
Identification in Companion Form. 9.3.2 Gradient Identification in
Companion Form. 9.4 Summary. Exercises. CHAPTER 10 ADAPTIVE FUZZY CONTROL.
10.1 Direct Adaptive Fuzzy Tracking Control. 10.2 Direct Adaptive Fuzzy
Model Reference Control. 10.3 Indirect Adaptive Fuzzy Tracking Control.
10.4 Indirect Adaptive Fuzzy Model Reference Control. 10.5 Adaptive
Feedback Linearization Control. 10.6 Summary. Exercises. REFERENCES.
APPENDIX COMPUTER PROGRAMS. INDEX.