Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/25907
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dc.contributor.authorKoçal, Osman Hilmi-
dc.date.accessioned2022-04-20T12:33:54Z-
dc.date.available2022-04-20T12:33:54Z-
dc.date.issued2012-05-
dc.identifier.citationHatun, M. ve Koçal, O. H. (2012). "Recursive Gauss-Seidel algorithm for direct self-tuning control". International Journal of Adaptive Control and Signal Processing, 26(5), 435-450.en_US
dc.identifier.issn0890-6327-
dc.identifier.issn1099-1115-
dc.identifier.urihttps://doi.org/10.1002/acs.1296-
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1002/acs.1296-
dc.identifier.urihttp://hdl.handle.net/11452/25907-
dc.description.abstractA recursive algorithm based on the use of GaussSeidel iterations is introduced to adjust the parameters of a self-tuning controller for minimum phase and a class of nonminimum phase discrete-time systems. The proposed algorithm is called the Recursive GaussSeidel (RGS) algorithm and is used to update the controller parameters directly. The use of the RGS algorithm with a generalized minimum variance control law is also given for nonminimum phase systems, and a forgetting factor is used to track the time-varying parameters. Furthermore, the overall stability of the closed-loop system is proven by using the Lyapunov stability theory. Using computer simulations, the performance of the RGS algorithm is examined and compared with the widely used recursive least squares algorithm.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutomation & control systemsen_US
dc.subjectEngineeringen_US
dc.subjectGauss-seidel algorithmen_US
dc.subjectSelf-tuning controlen_US
dc.subjectGeneralized minimum variance controlen_US
dc.subjectLyapunov stabilityen_US
dc.subjectAlgorithmsen_US
dc.subjectComputer simulationen_US
dc.subjectDigital control systemsen_US
dc.subjectDiscrete time control systemsen_US
dc.subjectController parameteren_US
dc.subjectDiscrete time systemen_US
dc.subjectForgetting factorsen_US
dc.subjectGauss Seidel iterationen_US
dc.subjectGauss-Seidelen_US
dc.subjectLyapunov stability theoryen_US
dc.subjectMinimum phaseen_US
dc.subjectNon-minimum phaseen_US
dc.subjectNon-minimum phase systemsen_US
dc.subjectRecursive algorithmsen_US
dc.subjectRecursive least square (rls)en_US
dc.subjectSelf tuning controlsen_US
dc.subjectSelf-tuning controllersen_US
dc.subjectTime varying parameteren_US
dc.subjectParameter estimationen_US
dc.subjectSquares parameter-estimationen_US
dc.subjectIterative solutionsen_US
dc.subjectIdentificationen_US
dc.titleRecursive Gauss-Seidel algorithm for direct self-tuning controlen_US
dc.typeArticleen_US
dc.identifier.wos000303978300005tr_TR
dc.identifier.scopus2-s2.0-84861191596tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Elektronik Mühendisliği Bölümü.tr_TR
dc.contributor.orcid0000-0003-0279-5508tr_TR
dc.identifier.startpage435tr_TR
dc.identifier.endpage450tr_TR
dc.identifier.volume26tr_TR
dc.identifier.issue5tr_TR
dc.relation.journalInternational Journal of Adaptive Control and Signal Processingen_US
dc.contributor.buuauthorHatun, Metin-
dc.contributor.researcheridAAH-2199-2021tr_TR
dc.relation.collaborationYurt içitr_TR
dc.subject.wosAutomation & control systemsen_US
dc.subject.wosEngineering, electrical & electronicen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ3en_US
dc.contributor.scopusid54684165800tr_TR
dc.subject.scopusStochastic Gradient; Recursive Identification; Autoregressive Moving Averageen_US
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