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Article: Adaptive H∞ control using backstepping design and neural networks
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TitleAdaptive H∞ control using backstepping design and neural networks
 
AuthorsNiu, Y3
Lam, J1
Wang, X3
Ho, DWC2
 
KeywordsBackstepping
Neural Network
Nonlinear Systems
 
Issue Date2005
 
PublisherA S M E International. The Journal's web site is located at http://ojps.aip.org/ASMEJournals/DynamicSys/
 
CitationJournal Of Dynamic Systems, Measurement And Control, Transactions Of The Asme, 2005, v. 127 n. 3, p. 478-485 [How to Cite?]
DOI: http://dx.doi.org/10.1115/1.1978905
 
AbstractIn this paper, the adaptive H∞ control problem based on the neural network technique is studied for a class of strict-feedback nonlinear systems with mismatching nonlinear uncertainties that may not be linearly parametrized. By combining the backstepping technique with H∞ control design, an adaptive neural controller is synthesized to attenuate the effect of approximation errors and guarantee an H∞ tracking performance for the closed-loop system. In this work, the structural property of the system is utilized to synthesize the controller such that the singularity problem of the controller usually encountered in feedback linearization design is avoided. A numerical simulation illustrating the H∞ control performance of the closed-loop system is provided. Copyright © 2005 by ASME.
 
ISSN0022-0434
2013 Impact Factor: 1.039
2013 SCImago Journal Rankings: 0.660
 
DOIhttp://dx.doi.org/10.1115/1.1978905
 
ISI Accession Number IDWOS:000232071400018
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorNiu, Y
 
dc.contributor.authorLam, J
 
dc.contributor.authorWang, X
 
dc.contributor.authorHo, DWC
 
dc.date.accessioned2012-08-08T08:43:58Z
 
dc.date.available2012-08-08T08:43:58Z
 
dc.date.issued2005
 
dc.description.abstractIn this paper, the adaptive H∞ control problem based on the neural network technique is studied for a class of strict-feedback nonlinear systems with mismatching nonlinear uncertainties that may not be linearly parametrized. By combining the backstepping technique with H∞ control design, an adaptive neural controller is synthesized to attenuate the effect of approximation errors and guarantee an H∞ tracking performance for the closed-loop system. In this work, the structural property of the system is utilized to synthesize the controller such that the singularity problem of the controller usually encountered in feedback linearization design is avoided. A numerical simulation illustrating the H∞ control performance of the closed-loop system is provided. Copyright © 2005 by ASME.
 
dc.description.natureLink_to_subscribed_fulltext
 
dc.identifier.citationJournal Of Dynamic Systems, Measurement And Control, Transactions Of The Asme, 2005, v. 127 n. 3, p. 478-485 [How to Cite?]
DOI: http://dx.doi.org/10.1115/1.1978905
 
dc.identifier.doihttp://dx.doi.org/10.1115/1.1978905
 
dc.identifier.epage485
 
dc.identifier.isiWOS:000232071400018
 
dc.identifier.issn0022-0434
2013 Impact Factor: 1.039
2013 SCImago Journal Rankings: 0.660
 
dc.identifier.issue3
 
dc.identifier.scopuseid_2-s2.0-25444511585
 
dc.identifier.spage478
 
dc.identifier.urihttp://hdl.handle.net/10722/156786
 
dc.identifier.volume127
 
dc.languageeng
 
dc.publisherA S M E International. The Journal's web site is located at http://ojps.aip.org/ASMEJournals/DynamicSys/
 
dc.publisher.placeUnited States
 
dc.relation.ispartofJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
 
dc.relation.referencesReferences in Scopus
 
dc.subjectBackstepping
 
dc.subjectNeural Network
 
dc.subjectNonlinear Systems
 
dc.titleAdaptive H∞ control using backstepping design and neural networks
 
dc.typeArticle
 
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<contributor.author>Wang, X</contributor.author>
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Author Affiliations
  1. The University of Hong Kong
  2. City University of Hong Kong
  3. East China University of Science and Technology