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

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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 languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Pages2813-2818
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2004
EventIECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society - Busan, Korea, Republic of
Duration: 2 Nov 20046 Nov 2004

Conference

ConferenceIECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society
CountryKorea, Republic of
CityBusan
Period2/11/046/11/04

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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