Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm

This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonline...

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Published inIEEE transactions on industrial electronics (1982) Vol. 51; no. 1; pp. 172 - 182
Main Authors Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Control systems
Control theory
Dynamical systems
Fuzzy control
Fuzzy logic
Fuzzy systems
Genetic algorithms
Mathematical models
Nonlinear control systems
Nonlinear dynamics
Nonlinear systems
Optimal control
Optimization
Stability
Uncertain systems
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ISSN0278-0046
1557-9948
DOI10.1109/TIE.2003.821898

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Summary:This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2003.821898