Fuzzy Modeling and Fuzzy Control [electronic resource] / by Huaguang Zhang, Derong Liu.

By: Zhang, Huaguang [author.]Contributor(s): Liu, Derong [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Control Engineering: Publisher: Boston, MA : Birkhäuser Boston, 2006Description: XIII, 416 p. 161 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780817645397Subject(s): Engineering | Computer science | Systems theory | Vibration | Engineering | Control Engineering | Systems Theory, Control | Mathematical Modeling and Industrial Mathematics | Vibration, Dynamical Systems, Control | Electronic and Computer Engineering | Models and PrinciplesAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
Contents:
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 of 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 ? 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.
In: Springer eBooksSummary: 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.
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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 of 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 ? 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.

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.

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