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Some Applications of Soft Computing Methods in System Modeling and Control
Bela Lantos
Department of Control Engineering and Information Technology Technical University of Budapest, H-1111 Budapest, Muegyetem rkp. 9, Hungary
Received:October 10, 1997Accepted:January 25, 1998Published:June 20, 1998
Keywords:Soft computing, Optimal controller parameter design, Neural robot control, Adaptive fazzy MIMO control
Abstract
The paper deals with the application of fuzzy systems, artificial neural networks (neural systems), and genetic algorithms to solve modeling and control problems in system engineering. Part 1 the paper covers the design of classical PID and fuzzy PID controllers for nonlinear systems with an (approximately) known dynamic model. Optimal controllers are designed based on genetic algorithms. Part 2 considers neural control of a SCARA robot. Part 3 deals with the fuzzy control of a special class of MIMO nonlinear systems and generalizes the method of Wang for such systems.
Cite this article as:B. Lantos, “Some Applications of Soft Computing Methods in System Modeling and Control,” J. Adv. Comput. Intell. Intell. Inform., Vol.2 No.3, pp. 82-87, 1998.Data files:
