Optimal and Stable Fuzzy Controllers for Nonlinear Systems Based on an Improved Genetic Algorithm

Hung Fat Frank Leung; H. K. Lam; S. H. Ling; Peter K S Tam

doi:10.1109/TIE.2003.821898

Optimal and Stable Fuzzy Controllers for Nonlinear Systems Based on an Improved Genetic Algorithm

Hung Fat Frank Leung, H. K. Lam, S. H. Ling, Peter K S Tam

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

44 Citations (Scopus)

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.

Original language	English
Pages (from-to)	172-182
Number of pages	11
Journal	IEEE Transactions on Industrial Electronics
Volume	51
Issue number	1
DOIs	https://doi.org/10.1109/TIE.2003.821898
Publication status	Published - 1 Feb 2004

Keywords

Fuzzy control
Nonlinear systems
Optimality and stability

ASJC Scopus subject areas

Electrical and Electronic Engineering
Instrumentation

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10.1109/TIE.2003.821898

http://hdl.handle.net/10397/1432

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AB - 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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Optimal and Stable Fuzzy Controllers for Nonlinear Systems Based on an Improved Genetic Algorithm

Abstract

Keywords

ASJC Scopus subject areas

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