Article: Adaptive H∞ control using backstepping design and neural networks

File Download
  • No File Attached
Links for fulltext
(May Require Subscription)
Supplementary
  • Basic View
  • Metadata View
  • XML View
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
2011 Impact Factor: 0.802
2011 SCImago Journal Rankings: 0.044
DOIhttp://dx.doi.org/10.1115/1.1978905
ISI Accession Number IDWOS:000232071400018
ReferencesReferences in Scopus
DC Field
Value
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
2011 Impact Factor: 0.802
2011 SCImago Journal Rankings: 0.044
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
Author Affiliations
  1. The University of Hong Kong
  2. City University of Hong Kong
  3. East China University of Science and Technology