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Adaptive Decentralized Control for Interconnected Time-Delay Uncertain Nonlinear Systems With Different Unknown Control Directions and Deferred Full-State Constraints

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dc.contributor.authorZhang, Liuliu-
dc.contributor.authorZhu, Lingchen-
dc.contributor.authorHua, Changchun-
dc.contributor.authorQian, Cheng-
dc.date.accessioned2024-01-01T02:00:16Z-
dc.date.available2024-01-01T02:00:16Z-
dc.date.issued2023-12-
dc.identifier.issn2162-237X-
dc.identifier.issn2162-2388-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89868-
dc.description.abstractIn this article, the problem of adaptive decentralized control is investigated for a class of interconnected time-delay uncertain nonlinear systems with different unknown control directions and deferred asymmetric time-varying (DTV) full-state constraints. By constructing the novel time-varying asymmetric integral barrier Lyapunov function (TVAIBLF), the conservative limitation of constant integral barrier Lyapunov function (IBLF) or symmetric IBLF is reduced and the need on the prior knowledge of control gains is also avoided, while the deferred constraints directly imposed on the states of system are achieved by introducing the shifting function into the controller design. Furthermore, based on the Nussbaum-type functions, a new adaptive decentralized control strategy for interconnected time-delay nonlinear systems with subsystems having different control directions is proposed via backstepping method. And it is proven that the proposed control method can guarantee that all signals in closed-loop system are bounded and the transform errors asymptotically converge to zero. Finally, the effectiveness of the proposed control strategy is illustrated through the simulation results.-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleAdaptive Decentralized Control for Interconnected Time-Delay Uncertain Nonlinear Systems With Different Unknown Control Directions and Deferred Full-State Constraints-
dc.typeArticle-
dc.identifier.wosid000795408800001-
dc.identifier.doi10.1109/TNNLS.2022.3171518-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.34, no.12, pp 10789 - 10801-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-85132520560-
dc.citation.endPage10801-
dc.citation.startPage10789-
dc.citation.titleIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS-
dc.citation.volume34-
dc.citation.number12-
dc.type.docTypeArticle-
dc.publisher.location미국-
dc.subject.keywordAuthorNonlinear systems-
dc.subject.keywordAuthorTime-varying systems-
dc.subject.keywordAuthorLyapunov methods-
dc.subject.keywordAuthorInterconnected systems-
dc.subject.keywordAuthorDigital TV-
dc.subject.keywordAuthorDecentralized control-
dc.subject.keywordAuthorBackstepping-
dc.subject.keywordAuthorDeferred full-state constraints-
dc.subject.keywordAuthorinterconnected time-delay systems-
dc.subject.keywordAuthortime-varying asymmetric integral barrier Lyapunov function (TVAIBLF)-
dc.subject.keywordAuthorunknown control directions-
dc.subject.keywordPlusOUTPUT-FEEDBACK CONTROL-
dc.subject.keywordPlusBARRIER LYAPUNOV FUNCTIONS-
dc.subject.keywordPlusFAULT-TOLERANT CONTROL-
dc.subject.keywordPlusNEURAL-NETWORK CONTROL-
dc.subject.keywordPlusTRACKING CONTROL-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusDESIGN-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Hardware & Architecture-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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