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Please use this identifier to cite or link to this item: https://doi.org/10.1109/ACC.1998.694631
DC FieldValue
dc.titleNeural-based adaptive control design for general nonlinear systems and its application to process Control
dc.contributor.authorGe, S.S.
dc.contributor.authorHang, C.C.
dc.contributor.authorZhang, T.
dc.date.accessioned2014-06-19T05:11:53Z
dc.date.available2014-06-19T05:11:53Z
dc.date.issued1998
dc.identifier.citationGe, S.S.,Hang, C.C.,Zhang, T. (1998). Neural-based adaptive control design for general nonlinear systems and its application to process Control. Proceedings of the American Control Conference 1 : 73-77. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ACC.1998.694631" target="_blank">https://doi.org/10.1109/ACC.1998.694631</a>
dc.identifier.isbn0780345304
dc.identifier.issn07431619
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72778
dc.description.abstractIn this work, a neural-based adaptive controller is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The proposed controller ensures that the output tracking error converges to a small neighborhood of the origin. The weight updating law of neural networks (NNs) is derived using Lyapunov theory and the stability of the closed-loop system is guaranteed. The proposed control scheme has been successfully applied to the composition control in a continuously stirred tank reactor (CSTR) in chemical processes. © 1998 AACC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ACC.1998.694631
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.doi10.1109/ACC.1998.694631
dc.description.sourcetitleProceedings of the American Control Conference
dc.description.volume1
dc.description.page73-77
dc.description.codenPRACE
dc.identifier.isiutNOT_IN_WOS
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