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dc.contributor.authorLin, P. -Z.en_US
dc.contributor.authorHsu, C. -F.en_US
dc.contributor.authorLee, T. -T.en_US
dc.contributor.authorWang, C. -H.en_US
dc.date.accessioned2014-12-08T15:10:35Z-
dc.date.available2014-12-08T15:10:35Z-
dc.date.issued2008-12-01en_US
dc.identifier.issn1751-8644en_US
dc.identifier.urihttp://dx.doi.org/10.1049/iet-cta:20070315en_US
dc.identifier.urihttp://hdl.handle.net/11536/8098-
dc.description.abstractA robust fuzzy-neural sliding-mode control (RFSC) scheme for unknown nonlinear systems is proposed. The RFSC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy-neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L(2) tracking performance. The SFNN identifier uses the structure- and parameter-learning phases to perform the estimation of the unknown system dynamics. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RFSC system is applied to three nonlinear dynamic systems. The simulation results show that the proposed RFSC system can achieve favourable tracking performance by incorporating SFNN identifier, sliding-mode control and robust control techniques.en_US
dc.language.isoen_USen_US
dc.titleRobust fuzzy-neural sliding-mode controller design via network structure adaptationen_US
dc.typeArticleen_US
dc.identifier.doi10.1049/iet-cta:20070315en_US
dc.identifier.journalIET CONTROL THEORY AND APPLICATIONSen_US
dc.citation.volume2en_US
dc.citation.issue12en_US
dc.citation.spage1054en_US
dc.citation.epage1065en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000262162700003-
dc.citation.woscount7-
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