Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2007.900660
DC FieldValue
dc.titleAdaptive control of mechanical systems using neural networks
dc.contributor.authorHuang, S.
dc.contributor.authorTan, K.K.
dc.contributor.authorLee, T.H.
dc.contributor.authorPutra, A.S.
dc.date.accessioned2014-06-17T02:36:42Z
dc.date.available2014-06-17T02:36:42Z
dc.date.issued2007-09
dc.identifier.citationHuang, S., Tan, K.K., Lee, T.H., Putra, A.S. (2007-09). Adaptive control of mechanical systems using neural networks. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 37 (5) : 897-903. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2007.900660
dc.identifier.issn10946977
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54891
dc.description.abstractIn this paper, we consider the decentralized adaptive control design problem for uncertain mechanical systems, where uncertainty may arise due to isolated subsystem and/or interconnections among subsystems. Radial basis function neural networks are used to approximate the nonlinear functions to include both dynamic and interconnection uncertainties in each subsystem. The stability of the thus designed control system can be guaranteed by a rigid proof. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2007.900660
dc.sourceScopus
dc.subjectAdaptive control
dc.subjectDecentralized control
dc.subjectMechanical systems
dc.subjectNeural networks (NNs)
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCC.2007.900660
dc.description.sourcetitleIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
dc.description.volume37
dc.description.issue5
dc.description.page897-903
dc.description.codenITCRF
dc.identifier.isiut000249023700014
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