This book offers a comprehensive introduction to intelligent control system design, using MATLAB simulation to verify typical intelligent controller designs. It also uses real-world case studies that present the results of intelligent controller implementations to illustrate the successful application of the theory. Addressing the need for systematic design approaches to intelligent control system design using neural network and fuzzy-based techniques, the book introduces the concrete design method and MATLAB simulation of intelligent control strategies; offers a catalog of implementable intelligent control design methods for engineering applications; provides advanced intelligent controller design methods and their stability analysis methods; and presents a sample simulation and Matlab program for each intelligent control algorithm.
The main topics addressed are expert control, fuzzy logic control, adaptive fuzzy control, neural network control, adaptive neural control and
intelligent optimization algorithms, providing several engineering application examples for each method.
Cuprins
Introduction.- Expert control.- Mathmatic foundation of fuzzy control.- Fuzzy logic control.- Adaptive fuzzy control.- Neural Network.- Typical Neural Network.- Senior Neural Network.- Neural network control with gradient descend.- Adaptive neural network control.- Digital RBF Neural Network Control.- Intelligent optimization algorithms.- Iterative learning control.
Despre autor
LIU Jinkun received his BS, MS and Ph D degrees from Northeastern University, Shenyang, China, in 1989, 1994 and 1997, respectively. He was a postdoctoral fellow at Zhejiang University from 1997 to 1999, and is currently a professor at Beihang University, China. He has published more than 100 research papers and eight books. His research interests include intelligent control and sliding mode control; partial differential equation (PDE) modeling and boundary control and application areas in motion control, such as flight control and robotic control, especially for under-actuated systems.
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