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Use este identificador para citar ou linkar para este item: http://repositorio.unb.br/handle/10482/43594
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dc.contributor.authorTognetti, Eduardo Stockler-
dc.contributor.authorLinhares, Tássio Melo-
dc.date.accessioned2022-05-02T13:34:02Z-
dc.date.available2022-05-02T13:34:02Z-
dc.date.issued2021-
dc.identifier.citationTOGNETTI, Eduardo S.; LINHARES, Tássio M. Dynamic output feedback controller design for uncertain Takagi–Sugeno fuzzy systems: a premise variable selection approach. IEEE Transactions on Fuzzy Systems, v. 29, n. 6, p. 1590-1600, jun. 2021. DOI: 10.1109/TFUZZ.2020.2981931.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/43594-
dc.language.isoInglêspt_BR
dc.publisherIEEEpt_BR
dc.rightsAcesso Restritopt_BR
dc.titleDynamic output feedback controller design for uncertain Takagi–Sugeno fuzzy systems : a premise variable selection approachpt_BR
dc.typeArtigopt_BR
dc.subject.keywordSistemas difusospt_BR
dc.subject.keywordSistemas de controlept_BR
dc.identifier.doi10.1109/TFUZZ.2020.2981931pt_BR
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9042250-
dc.description.abstract1This article presents new design conditions of full-order dynamic output feedback controllers for continuous-time Takagi-Sugeno (T-S) fuzzy systems allowing the selection of premise variables to be used in the control law. The fuzzy output controller is allowed to have a different number of fuzzy rules and a different set of membership functions from the T-S model. This includes the cases of complete or partial immeasurable premise variables. The main aspect of the proposed methodology is to present conditions such that the control gains are independent of the premise variables that cannot be measured allowing flexibility for the designer in a realistic output feedback context. For this purpose, the design conditions are expressed as linear matrix inequality relaxations combined with scalar parameters that provide extra degrees of freedom. The proposed control methodology also deals with model uncertainties and the use of fuzzy Lyapunov functions. The effectiveness and applicability of the methodology are shown through numerical examples.pt_BR
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