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

Leung, FHF; Lam, HK; Ling, SH; Tam, PKS

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

Leung, FHF Lam, HK Ling, SH

Tam, PKS

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2004, 51 (1), pp. 172 - 182
Issue Date:: 2004-02-01

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Field	Value	Language
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Tam, PKS	en_US
dc.date.issued	2004-02-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2004, 51 (1), pp. 172 - 182	en_US
dc.identifier.issn	0278-0046	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15259
dc.description.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.	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Electronics	en_US
dc.relation.isbasedon	10.1109/TIE.2003.821898	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Optimal and Stable Fuzzy Controllers for Nonlinear Systems Based on an Improved Genetic Algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	51	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	WOS:000188808100019	en_US
dc.location.activity	WOS:000289949000090
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	51	en_US

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.

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http://hdl.handle.net/10453/15259