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Informationen zum Autor 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. Klappentext 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. Zusammenfassung A fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1. This text provides a broad introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systems. Inhaltsverzeichnis 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. ...
