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

TitleAdaptive H∞ control using backstepping design and neural networks
Authors
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/
Citation
Journal Of Dynamic Systems, Measurement And Control, Transactions Of The Asme, 2005, v. 127 n. 3, p. 478-485 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/156786
ISSN
2014 Impact Factor: 0.978
2014 SCImago Journal Rankings: 0.698
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorNiu, Yen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorWang, Xen_US
dc.contributor.authorHo, DWCen_US
dc.date.accessioned2012-08-08T08:43:58Z-
dc.date.available2012-08-08T08:43:58Z-
dc.date.issued2005en_US
dc.identifier.citationJournal Of Dynamic Systems, Measurement And Control, Transactions Of The Asme, 2005, v. 127 n. 3, p. 478-485en_US
dc.identifier.issn0022-0434en_US
dc.identifier.urihttp://hdl.handle.net/10722/156786-
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.en_US
dc.languageengen_US
dc.publisherA S M E International. The Journal's web site is located at http://ojps.aip.org/ASMEJournals/DynamicSys/en_US
dc.relation.ispartofJournal of Dynamic Systems, Measurement and Control, Transactions of the ASMEen_US
dc.subjectBacksteppingen_US
dc.subjectNeural Networken_US
dc.subjectNonlinear Systemsen_US
dc.titleAdaptive H∞ control using backstepping design and neural networksen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1115/1.1978905en_US
dc.identifier.scopuseid_2-s2.0-25444511585en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-25444511585&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume127en_US
dc.identifier.issue3en_US
dc.identifier.spage478en_US
dc.identifier.epage485en_US
dc.identifier.isiWOS:000232071400018-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridNiu, Y=7202225475en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridWang, X=8885333800en_US
dc.identifier.scopusauthoridHo, DWC=7402971938en_US

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