Read more
Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T-S) fuzzy model-based approaches receiving the greatest attention.
Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T-S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover:
T-S fuzzy modeling and identification via nonlinear models or data
Stability analysis of T-S fuzzy systems
Stabilization controller synthesis as well as robust H¿ and observer and output feedback controller synthesis
Robust controller synthesis of uncertain T-S fuzzy systems
Time-delay T-S fuzzy systems
Fuzzy model predictive control
Robust fuzzy filtering
Adaptive control of T-S fuzzy systems
A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB®.
List of contents
Introduction to Fuzzy Logic Control. Fuzzy Sets and Fuzzy Systems. T–S Fuzzy Modeling and Identification. Stability Analysis of T–S Fuzzy Systems. Stabilization Controller Synthesis of T–S Fuzzy Systems. Robust H∞ Controller Synthesis of T–S Fuzzy Systems. Observer and Output Feedback Controller Synthesis of T–S Fuzzy Systems. Robust Controller Synthesis of Uncertain T–S Fuzzy Systems. Controller Synthesis of T–S Fuzzy Systems with Time-Delay. Fuzzy Model Predictive Control. Robust Filtering of T–S Fuzzy Systems. Adaptive Control of T–S Fuzzy Systems.
About the author
Gang (Gary) Feng is Associate Provost and Chair Professor of Mechatronic Engineering at the City University of Hong Kong. He is also the Cheung Kong Chair Professor at Nanjing University of Science and Technology conferred by the Education Ministry of China. Dr. Feng has done research work in adaptive control, robot control, intelligent control, and, more recently, control of piecewise linear systems and switched systems. His research work has led to the publication of one edited book, seven invited book chapters, and over 180 international journal papers and numerous international conference papers. He has received the IEEE Transactions on Fuzzy Systems Outstanding Paper Award (2007), an Alexander von Humboldt Research Fellowship of Germany (1997), and a number of best conference paper awards. He has been invited to give plenary/special lectures at a number of international conferences. Dr. Feng is an IEEE Fellow and an associate editor of IEEE Transactions on Automatic Control, IEEE Transactions on Fuzzy Systems, and Mechatronics. He has served as an associate editor of IEEE Transactions on Systems, Man, & Cybernetics, Part C, Control Theory and Applications, and on the Conference Editorial Board, IEEE Control Systems Society.
Summary
Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T–S) fuzzy model-based approaches receiving the greatest attention.
Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T–S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover:
- T–S fuzzy modeling and identification via nonlinear models or data
- Stability analysis of T–S fuzzy systems
- Stabilization controller synthesis as well as robust H∞ and observer and output feedback controller synthesis
- Robust controller synthesis of uncertain T–S fuzzy systems
- Time-delay T–S fuzzy systems
- Fuzzy model predictive control
- Robust fuzzy filtering
- Adaptive control of T–S fuzzy systems
A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB®.
