Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72897
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dc.titleRobust model reference adaptive control of robots based on neural network parametrization
dc.contributor.authorGe, S.S.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-19T05:13:12Z
dc.date.available2014-06-19T05:13:12Z
dc.date.issued1997
dc.identifier.citationGe, S.S.,Lee, T.H. (1997). Robust model reference adaptive control of robots based on neural network parametrization. Proceedings of the American Control Conference 3 : 2006-2010. ScholarBank@NUS Repository.
dc.identifier.issn07431619
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72897
dc.description.abstractIn this paper, a robust model reference adaptive controller is presented for robots based on neural network parametrization. The controller is based on applying direct adaptive techniques to a basic fixed controller for better control performance, while a sliding mode control is introduced to guarantee robust closed-loop stability. It is shown that if Bounded Basis Function (BBF) networks are used for the parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleProceedings of the American Control Conference
dc.description.volume3
dc.description.page2006-2010
dc.description.codenPRACE
dc.identifier.isiutNOT_IN_WOS
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