Ebook: Advances in Fuzzy Control

Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.

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Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.

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Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.
Content:
Front Matter....Pages I-VIII
Introduction....Pages 1-10
Global Stability Analysis of Second-Order Fuzzy Systems....Pages 11-31
Quadratic Stability of Continuous-Time Fuzzy Control Systems....Pages 33-65
Fuzzy Stability Analysis of Fuzzy Systems: A Lyapunov Approach....Pages 67-101
Inverse Fuzzy Process Models for Robust Hybrid Control....Pages 103-127
Inverse Fuzzy Model Based Predictive Control....Pages 129-154
Stable Adaptive Control Using Fuzzy Systems and Neural Networks....Pages 155-187
An Adaptive Fuzzy Sliding-Mode Controller....Pages 189-223
Discrete-Time Adaptive Fuzzy Logic Control of Feedback Linearizable Systems....Pages 225-261
Fuzzy Model Reference Learning Control....Pages 263-282
Development of a Fuzzy Relational-Based Predictive Controller....Pages 283-315
A Simplified Fuzzy Relational Structure for Adaptive Predictive Control....Pages 317-336
Predictive Control Based on a Fuzzy Model....Pages 337-356
Transient Performance, Robustness and Off-Equilibrium Linearisation in Fuzzy Gain Scheduled Control....Pages 357-375
Design of Fuzzy Gain Schedulers....Pages 377-417
Back Matter....Pages 418-421

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Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.
Content:
Front Matter....Pages I-VIII
Introduction....Pages 1-10
Global Stability Analysis of Second-Order Fuzzy Systems....Pages 11-31
Quadratic Stability of Continuous-Time Fuzzy Control Systems....Pages 33-65
Fuzzy Stability Analysis of Fuzzy Systems: A Lyapunov Approach....Pages 67-101
Inverse Fuzzy Process Models for Robust Hybrid Control....Pages 103-127
Inverse Fuzzy Model Based Predictive Control....Pages 129-154
Stable Adaptive Control Using Fuzzy Systems and Neural Networks....Pages 155-187
An Adaptive Fuzzy Sliding-Mode Controller....Pages 189-223
Discrete-Time Adaptive Fuzzy Logic Control of Feedback Linearizable Systems....Pages 225-261
Fuzzy Model Reference Learning Control....Pages 263-282
Development of a Fuzzy Relational-Based Predictive Controller....Pages 283-315
A Simplified Fuzzy Relational Structure for Adaptive Predictive Control....Pages 317-336
Predictive Control Based on a Fuzzy Model....Pages 337-356
Transient Performance, Robustness and Off-Equilibrium Linearisation in Fuzzy Gain Scheduled Control....Pages 357-375
Design of Fuzzy Gain Schedulers....Pages 377-417
Back Matter....Pages 418-421
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