Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.839354
DC FieldValue
dc.titleNeural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form
dc.contributor.authorWang, D.
dc.contributor.authorHuang, J.
dc.date.accessioned2014-11-28T01:52:17Z
dc.date.available2014-11-28T01:52:17Z
dc.date.issued2005-01
dc.identifier.citationWang, D., Huang, J. (2005-01). Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks 16 (1) : 195-202. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.839354
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/111444
dc.description.abstractThe dynamic surface control (DSC) technique was developed recently by Swaroop et al. This technique simplified the backstepping design for the control of nonlinear systems in strict-feedback form by overcoming the problem of "explosion of complexity" It was later extended to adaptive backstepping design for nonlinear systems with linearly parameterized uncertainty. In this paper, by incorporating this design technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing method. In addition, a stability analysis is given which shows that our control law can guarantee the uniformly ultimate boundedness of the solution of the closed-loop system, and make the tracking error arbitrarily small. © 2005 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNN.2004.839354
dc.sourceScopus
dc.subjectAdaptive control
dc.subjectNeural networks
dc.subjectNonlinear control
dc.subjectStrict-feedback systems
dc.typeArticle
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1109/TNN.2004.839354
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume16
dc.description.issue1
dc.description.page195-202
dc.description.codenITNNE
dc.identifier.isiut000226621900015
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