Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAC.2004.837561
DC FieldValue
dc.titleDirect adaptive control for a class of MIMO nonlinear systems using neural networks
dc.contributor.authorGe, S.S.
dc.contributor.authorLi, G.Y.
dc.contributor.authorZhang, J.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:45:28Z
dc.date.available2014-06-17T02:45:28Z
dc.date.issued2004-11
dc.identifier.citationGe, S.S., Li, G.Y., Zhang, J., Lee, T.H. (2004-11). Direct adaptive control for a class of MIMO nonlinear systems using neural networks. IEEE Transactions on Automatic Control 49 (11) : 2001-2006. ScholarBank@NUS Repository. https://doi.org/10.1109/TAC.2004.837561
dc.identifier.issn00189286
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55643
dc.description.abstractIn this note, direct adaptive neural network (NN) control is studied for a class of multiple-input-multiple-output nonlinear systems based on input-output discrete-time model with unknown interconnections between subsystems. By finding an orthogonai matrix to tune the NN weights, the closed-loop system is proven to be semiglobally uniformly ultimately bounded. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameter. © 2004 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TAC.2004.837561
dc.sourceScopus
dc.subjectAdaptive control
dc.subjectDiscrete-time systems
dc.subjectMultiple-input-multiple-output (MIMO) systems
dc.subjectNeural networks (NNs)
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentBIOENGINEERING
dc.description.doi10.1109/TAC.2004.837561
dc.description.sourcetitleIEEE Transactions on Automatic Control
dc.description.volume49
dc.description.issue11
dc.description.page2001-2006
dc.description.codenIETAA
dc.identifier.isiut000225086400011
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