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dc.contributor.authorRomano, Rodrigo Alvite
dc.contributor.authorPait, Felipe
dc.date.accessioned2024-10-15T21:11:10Z
dc.date.available2024-10-15T21:11:10Z
dc.date.issued2017
dc.identifier.issn0018-9286
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018309755&doi=10.1109%2fTAC.2016.2602891&partnerID=40&md5=8f54f0080e9bc42f48846d8146145bd0
dc.identifier.urihttps://repositorio.maua.br/handle/MAUA/1282
dc.description.abstractIdentification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. Approaching the first using matchable-observable models originally developed in the adaptive control literature and the second via direct or derivative-free optimization, effective least-squares algorithms can be used for parameter estimation. The accuracy, robustness and moderate computational demands of the methods proposed are demonstrated via simulations with randomly generated models and applied to identification using real process data. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature. © 1963-2012 IEEE.en
dc.languageInglêspt_BR
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.ispartofIEEE Transactions on Automatic Control
dc.rightsAcesso Restrito
dc.sourceScopusen
dc.subjectDirect optimizationen
dc.subjectmultivariable systemsen
dc.subjectparameter estimationen
dc.subjectsystem identificationen
dc.subjectBandpass filtersen
dc.subjectIdentification (control systems)en
dc.subjectMultivariable systemsen
dc.subjectOptimizationen
dc.subjectAdaptive Controlen
dc.subjectComputational demandsen
dc.subjectDerivative-free optimizationen
dc.subjectDirect optimizationen
dc.subjectFilter designsen
dc.subjectLeast squares algorithmen
dc.subjectLinear time invarianten
dc.subjectMultivariable identificationsen
dc.subjectParameter estimationen
dc.titleMatchable-Observable Linear Models and Direct Filter Tuning: An Approach to Multivariable Identificationen
dc.typeArtigo de Periódicopt_BR
dc.identifier.doi10.1109/TAC.2016.2602891
dc.description.affiliationEscola de Engenharia Maua, Instituto Maua de Tecnologia Sao Caetano Do sul SP, Brazil
dc.description.affiliationEscola Politecnica, Universidade de S Ao Paulo Sao, Paulo SP, Brazil
dc.identifier.scopus2-s2.0-85018309755
dc.citation.issue5
dc.citation.epage2193
dc.citation.spage2180
dc.citation.volume62


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