Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-cta:19952122
DC FieldValue
dc.titleDirect neural control system: Nonlinear extension of adaptive control
dc.contributor.authorYuan, M.
dc.contributor.authorPoo, A.N.
dc.contributor.authorHong, G.S.
dc.date.accessioned2014-06-17T05:11:02Z
dc.date.available2014-06-17T05:11:02Z
dc.date.issued1995-11
dc.identifier.citationYuan, M., Poo, A.N., Hong, G.S. (1995-11). Direct neural control system: Nonlinear extension of adaptive control. IEE Proceedings: Control Theory and Applications 142 (6) : 661-667. ScholarBank@NUS Repository. https://doi.org/10.1049/ip-cta:19952122
dc.identifier.issn13502379
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/58113
dc.description.abstractThe methodology of design of a conventional model-reference-adaptive-control system is extended to design a direct neural control for a class of nonlinear system with structural uncertainty. A structured feedforward neural network, a Sigmoid-linear network, is used as the controller, which can be interpreted as a nonlinear extension of the conventional adaptive control. Without a specific pretraining stage, the weights of the neural network are adjusted online to minimize the error between the plant output and the desired output signal, according to a learning law derived in light of gradient-descent method. The local stability can be achieved provided that proper conditions are satisfied for the system. Simulation studies are carried out for linear and nonlinear plants, respectively, and verify the applicability of the proposed control strategy.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1049/ip-cta:19952122
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.description.doi10.1049/ip-cta:19952122
dc.description.sourcetitleIEE Proceedings: Control Theory and Applications
dc.description.volume142
dc.description.issue6
dc.description.page661-667
dc.description.codenICTAE
dc.identifier.isiutA1995TM11100018
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