Journal
JOURNAL OF VIBRATION AND CONTROL
Volume 18, Issue 12, Pages 1886-1899Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1077546311428345
Keywords
Artificial intelligence; chaotic system; linear differential inclusion; Lyapunov method
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Funding
- National Science Council of the Republic of China, Taiwan [NSC 100-2221-E-022-013-MY2, NSC 100-2628-E-022-002-MY2, NSC 98-2221-E-366-006-MY2]
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In this study we present a neural network (NN)-based approach to represent a nonlinear Takagi-Sugeno system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear systems. The controller design is based on the fuzzy control and parallel distributed compensation scheme, which is utilized to construct a global fuzzy logic controller by blending all local state-feedback controllers. Furthermore, this control problem can be reduced to linear matrix inequality problems by the Schur Complements. Efficient interior-point algorithms are now available in the Matlab toolbox to solve this problem. A chaotic system is simulated to show the feasibility of the proposed fuzzy controller design approach.
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