Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems

H. K. Lam; Hung Fat Frank Leung

doi:10.1109/IECON.2004.1432254

Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems

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

2 Citations (Scopus)

Abstract

This paper presents the stability analysis, synthesis, and performance optimization of a radial-basis-function neural-network based control system. Global stability conditions will be derived in terms of matrix measure. Based on the derived stability conditions, connection weights of the radial-basis-function neural-network based controller can be optimized by genetic algorithm (GA) subject to the system stability. Furthermore, the system performance will also be optimized by the GA. An application example on stabilizing an inverted pendulum will be given to illustrate the design procedure and merits of the proposed approach.

Original language	English
Title of host publication	IECON Proceedings (Industrial Electronics Conference)
Pages	2813-2818
Number of pages	6
DOIs	https://doi.org/10.1109/IECON.2004.1432254
Publication status	Published - 1 Dec 2004
Event	IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society - Busan, Korea, Republic of Duration: 2 Nov 2004 → 6 Nov 2004

Conference

Conference	IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society
Country/Territory	Korea, Republic of
City	Busan
Period	2/11/04 → 6/11/04

ASJC Scopus subject areas

Electrical and Electronic Engineering

More information

10.1109/IECON.2004.1432254

http://hdl.handle.net/10397/1395

Cite this

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title = "Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems",

abstract = "This paper presents the stability analysis, synthesis, and performance optimization of a radial-basis-function neural-network based control system. Global stability conditions will be derived in terms of matrix measure. Based on the derived stability conditions, connection weights of the radial-basis-function neural-network based controller can be optimized by genetic algorithm (GA) subject to the system stability. Furthermore, the system performance will also be optimized by the GA. An application example on stabilizing an inverted pendulum will be given to illustrate the design procedure and merits of the proposed approach.",

author = "Lam, {H. K.} and Leung, {Hung Fat Frank}",

year = "2004",

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doi = "10.1109/IECON.2004.1432254",

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Lam, HK & Leung, HFF 2004, Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems. in IECON Proceedings (Industrial Electronics Conference)., FD6-5, pp. 2813-2818, IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society, Busan, Korea, Republic of, 2/11/04. https://doi.org/10.1109/IECON.2004.1432254

Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems. / Lam, H. K.; Leung, Hung Fat Frank.

IECON Proceedings (Industrial Electronics Conference). 2004. p. 2813-2818 FD6-5.

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

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Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems

Abstract

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