Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm

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

Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm

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

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

This paper tackles the control problem of nonlinear systems subject to parameter uncertainties based on a fuzzy logic approach and the genetic algorithm (GA). In order to achieve a stable controller, TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller and the corresponding stability conditions will be derived. The parameters of the fuzzy controller and the solution to the stability conditions are determined using GA. In order to obtain the optimal performance, the membership functions of the fuzzy controller are obtained automatically by minimizing a defined fitness function using GA.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Pages	908-911
Number of pages	4
Publication status	Published - 1 Dec 2001
Event	10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia Duration: 2 Dec 2001 → 5 Dec 2001

Conference

Conference	10th IEEE International Conference on Fuzzy Systems
Country/Territory	Australia
City	Melbourne
Period	2/12/01 → 5/12/01

ASJC Scopus subject areas

Software
Safety, Risk, Reliability and Quality
Chemical Health and Safety

More information

http://hdl.handle.net/10397/1414

Cite this

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title = "Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm",

abstract = "This paper tackles the control problem of nonlinear systems subject to parameter uncertainties based on a fuzzy logic approach and the genetic algorithm (GA). In order to achieve a stable controller, TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller and the corresponding stability conditions will be derived. The parameters of the fuzzy controller and the solution to the stability conditions are determined using GA. In order to obtain the optimal performance, the membership functions of the fuzzy controller are obtained automatically by minimizing a defined fitness function using GA.",

author = "Lam, {H. K.} and Ling, {S. H.} and Leung, {Hung Fat Frank} and Tam, {P. K S}",

year = "2001",

month = dec,

day = "1",

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Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm. / Lam, H. K.; Ling, S. H.; Leung, Hung Fat Frank et al.
IEEE International Conference on Fuzzy Systems. 2001. p. 908-911.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Tam, P. K S

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N2 - This paper tackles the control problem of nonlinear systems subject to parameter uncertainties based on a fuzzy logic approach and the genetic algorithm (GA). In order to achieve a stable controller, TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller and the corresponding stability conditions will be derived. The parameters of the fuzzy controller and the solution to the stability conditions are determined using GA. In order to obtain the optimal performance, the membership functions of the fuzzy controller are obtained automatically by minimizing a defined fitness function using GA.

AB - This paper tackles the control problem of nonlinear systems subject to parameter uncertainties based on a fuzzy logic approach and the genetic algorithm (GA). In order to achieve a stable controller, TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller and the corresponding stability conditions will be derived. The parameters of the fuzzy controller and the solution to the stability conditions are determined using GA. In order to obtain the optimal performance, the membership functions of the fuzzy controller are obtained automatically by minimizing a defined fitness function using GA.

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T2 - 10th IEEE International Conference on Fuzzy Systems

Y2 - 2 December 2001 through 5 December 2001

ER -

Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithm

Abstract

Conference

ASJC Scopus subject areas

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