Fuzzy Control and IdentificationISBN: 9780470542774
231 pages
December 2010

Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacleavoidance controller for an autonomous vehicle using modus ponendo tollens logic.
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 IfThen 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 TNorm, COG Defuzzification.
3.6.2 Wind Chill Calculation, Minimum TNorm, CA Defuzzification.
3.6.3 Wind Chill Calculation, Product TNorm, COG Defuzzification.
3.6.4 Wind Chill Calculation, Product TNorm, CA Defuzzification.
3.6.5 Wind Chill Calculation, Singleton Output Fuzzy Sets, Product TNorm, 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 ContinuousTime Model Forms.
5.1.1 Nonlinear TimeInvariant ContinuousTime StateSpace Models.
5.1.2 Linear TimeInvariant ContinuousTime StateSpace Models.
5.2 Model Forms for DiscreteTime Systems.
5.2.1 Input–Output Difference Equation Model for Linear DiscreteTime Systems.
5.2.2 Linear TimeInvariant DiscreteTime StateSpace 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 ContinuousTime Linear StateSpace Dynamic Systems.
6.3 Takagi–Sugeno Fuzzy Systems as Interpolators between DiscreteTime Linear StateSpace Dynamic Systems.
6.4 Takagi–Sugeno Fuzzy Systems as Interpolators between DiscreteTime Dynamic Systems described by Input–Output Difference Equations.
6.5 Summary.
Exercises.
CHAPTER 7 PARALLEL DISTRIBUTED CONTROL WITH TAKAGI–SUGENO FUZZY SYSTEMS.
7.1 ContinuousTime Systems.
7.2 DiscreteTime 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 LeastSquares Estimation.
8.1.1 Batch Least Squares.
8.1.2 Recursive Least Squares.
8.2 Batch LeastSquares Fuzzy Estimation in Mamdani Form.
8.3 Recursive LeastSquares Fuzzy Estimation in Mamdani Form.
8.4 LeastSquares 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 LeastSquares Identification in Difference Equation Form.
9.2.2 Recursive LeastSquares 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 LeastSquares 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.
“This is a very useful and attractive material on fuzzy sets in control engineeringaccessible to large categories of readers … The book is equally recommended to students (who want to become familiar with the fuzzy logic approach), educators (who are looking for a reliable course and / or application support) and practitioners (who are interested in enlarging their professional horizon).” (Zentralblatt MATH, 2012)