Fuzzy Modeling and Fuzzy Control

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems.

Based on three types of fuzzy models-the Mamdani fuzzy model, the Takagi-Sugeno fuzzy model, and the fuzzy hyperbolic model-the book addresses a number of important issues in fuzzy control systems, including fuzzy modeling, fuzzy inference, stability analysis, systematic design frameworks, robustness, and optimality. The authors develop several advanced control schemes, such as the fuzzy model-based generalized predictive control scheme, the fuzzy adaptive control scheme based on fuzzy basis function vectors, the fuzzy control scheme based on fuzzy performance evaluators, and the fuzzy sliding-mode control scheme. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

Texte du rabat

Fuzzy logic methodology has proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology is applicable to many real-world problems, especially in the area of consumer products. This book presents the first comprehensive, unified treatment of fuzzy modeling and fuzzy control, providing tools for the control of complex nonlinear systems. Coverage includes model complexity, model precision, and computing time. This is an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, and also useful for graduate courses in electrical engineering, computer engineering, and computer science.

Contenu

Preface
Fuzzy Set Theory and Rough Set Theory
Identification of the Takagi-Sugeno Fuzzy Model
Fuzzy Model Identification Based on Rough Set Data Analysis
Identification of the Fuzzy Hyperbolic Model
Basic Methods for Fuzzy Inference and Control
Fuzzy Inference and Control Methods Involving Two Kinds of Uncertainties
Fuzzy Control Schemes via a Fuzzy Performance Evaluator
Multivariable Predictive Control Based on the T-S Fuzzy Model
Adaptive Control Methods Based on Fuzzy Basis Function Vectors
Controller Design Based on the Fuzzy Hyperbolic Model
Fuzzy H-infinity Filter Design for Nonlinear Discrete-Time Systems with Multiple Time-Delays
Chaotification of the Fuzzy Hyperbolic Model
Feedforward Fuzzy Control Approach Using the Fourier Integral
Index