NDLI: Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

Content Provider	Springer Nature Link
Author	Lee, Choon Young Eom, Tae Dok Lee, Ju Jang
Copyright Year	2001
Abstract	This paper proposes another adaptive control scheme for nonlinear systems using a Takagi-Sugeno fuzzy model. Takagi-Sugeno fuzzy models have been widely used to identify the structures and parameters of unknown or partially known plants, and to control nonlinear systems. This scheme shows a good approximation capability by the fuzzy blending of local dynamics. Since a Takagi-Sugeno fuzzy model is a nonlinear system in nature, and its parameters are not linearly parameterized, it is difficult to design an adaptive controller using conventional design methods for adaptive controllers which are derived from linearly parameterized systems. In this paper, the functional form of the local dynamics are assumed to be known, but the corresponding parameters are unknown. This additional information about system nonlinearity makes it possible to design an adaptive controller for a nonlinearly parameterized system. The control law is similar to that of a conventional adaptive control technique, while its parameter-update rule is based on the local search method. A parameter-update law is derived so that the time-derivative of the Lyapunov function is negative in the region of interest. Simulation results have shown that this adaptive controller is capable of a good performance.
Starting Page	20
Ending Page	25
Page Count	6
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	5
Issue Number	1
e-ISSN	16147456
Language	English
Publisher	Springer-Verlag
Publisher Date	2001-01-01
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy control Adaptive control Takagi-Sugeno fuzzy model Nonlinearly parameterized system Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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