Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0959-1524(98)00054-7
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dc.titleNonlinear adaptive control using neural networks and its application to CSTR systems
dc.contributor.authorGe, S.S.
dc.contributor.authorHang, C.C.
dc.contributor.authorZhang, T.
dc.date.accessioned2014-10-07T03:01:41Z
dc.date.available2014-10-07T03:01:41Z
dc.date.issued1999-08
dc.identifier.citationGe, S.S., Hang, C.C., Zhang, T. (1999-08). Nonlinear adaptive control using neural networks and its application to CSTR systems. Journal of Process Control 9 (4) : 313-323. ScholarBank@NUS Repository. https://doi.org/10.1016/S0959-1524(98)00054-7
dc.identifier.issn09591524
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/80823
dc.description.abstractIn this paper, adaptive tracking control is considered for a class of general nonlinear systems using multilayer neural networks (MNNs). Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is established based on implicit function theory. Then, MNNs are introduced to reconstruct this ideal IFLC to approximately realize feedback linearization. The proposed adaptive controller ensures that the system output tracks a given bounded reference signal and the tracking error converges to an ε-neighborhood of zero with ε being a small design parameter, while stability of the closed-loop system is guaranteed. The effectiveness of the proposed controller is illustrated through an application to composition control in a continuously stirred tank reactor (CSTR) system.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0959-1524(98)00054-7
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.doi10.1016/S0959-1524(98)00054-7
dc.description.sourcetitleJournal of Process Control
dc.description.volume9
dc.description.issue4
dc.description.page313-323
dc.description.codenJPCOE
dc.identifier.isiut000079624800005
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