Fuzzy Modeling and Fuzzy Control (Control Engineering) - Hardcover

Über die Autorin bzw. den Autor

Hongjing Liang received his B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, his M.S. degree in fundamental mathematics from Northeastern University, Shenyang, China, in 2011, and a Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2016. He is currently working at Bohai University. His research interests include multi-agent systems, complex systems, and output regulation. Huaguang Zhang received his B.S. and M.S. degrees in control engineering from Northeastern Electric Power University, Jilin, China, 1982 and 1985, respectively, and a Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. Dr. Zhang joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a Postdoctoral Fellow. Since 1994, he has been a professor and the head of the Electric Automation Institute, Northeastern University. He has authored three English monographs and holds 30 patents. His main research interests are neural network-based control, fuzzy control, chaos control, nonlinear control, signal processing, adaptive dynamic programming (ADP) and their industrial applications. Dr. Zhang was a recipient of the Nationwide Excellent Post-Doctor, the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003, the Cheung Kong Scholar Award from the Education Ministry of China in 2005, and the IEEE Transactions on Neural Networks Outstanding Paper Award in 2012. He was an Associate Editor of Automatica, IEEE Transactions on Cybernetics and Neurocomputing. He is the Deputy Director of the Intelligent System Engineering Committee of Chinese Association of Artificial Intelligence.

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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.