Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1432
Title: Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm
Authors: Leung, FHF 
Lam, HK
Ling, SH
Tam, PKS
Keywords: Fuzzy control
Nonlinear systems
Optimality and stability
Issue Date: Feb-2004
Publisher: IEEE
Source: IEEE transactions on industrial electronics, Feb. 2004, v. 51, no. 1, p. 172-182 How to cite?
Journal: IEEE transactions on industrial electronics 
Abstract: 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.
URI: http://hdl.handle.net/10397/1432
ISSN: 0278-0046
DOI: 10.1109/TIE.2003.821898
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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