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Please use this identifier to cite or link to this item: https://doi.org/10.1109/72.977306
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dc.titleDirect adaptive NN control of a class of nonlinear systems
dc.contributor.authorGe, S.S.
dc.contributor.authorWang, C.
dc.date.accessioned2014-06-17T02:45:29Z
dc.date.available2014-06-17T02:45:29Z
dc.date.issued2002-01
dc.identifier.citationGe, S.S., Wang, C. (2002-01). Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks 13 (1) : 214-221. ScholarBank@NUS Repository. https://doi.org/10.1109/72.977306
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55645
dc.description.abstractIn this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing a special property of the affine term, the developed scheme avoids the controller singularity problem completely. All the signals in the closed loop are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/72.977306
dc.sourceScopus
dc.subjectAdaptive control
dc.subjectBackstepping
dc.subjectNeural control
dc.subjectNeural network (NN)
dc.subjectUncertain strict-feedback system
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/72.977306
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume13
dc.description.issue1
dc.description.page214-221
dc.description.codenITNNE
dc.identifier.isiut000173440100018
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