New framework for identifying discrete‑time switched linear systems

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Brasília, Faculty of Gama-
Autor(es): dc.contributorUniversity of Brasília, Faculty of Technology, Department of Electrical Engineering-
Autor(es): dc.contributorUniversity of Brasília, Faculty of Technology, Department of Electrical Engineering-
Autor(es): dc.creatorLopes, Renato Vilela-
Autor(es): dc.creatorIshihara, João Yoshiyuki-
Autor(es): dc.creatorBorges, Geovany Araújo-
Data de aceite: dc.date.accessioned2024-10-23T15:30:08Z-
Data de disponibilização: dc.date.available2024-10-23T15:30:08Z-
Data de envio: dc.date.issued2024-01-21-
Data de envio: dc.date.issued2024-01-21-
Data de envio: dc.date.issued2023-11-07-
Fonte completa do material: dc.identifierhttp://repositorio2.unb.br/jspui/handle/10482/47430-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s40430-023-04505-2-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-8824-6384-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-5916-0207-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0003-4265-9471-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/884710-
Descrição: dc.descriptionThis paper addresses the problem of offline identification of a particular class of hybrid dynamical systems that are discrete-time switched linear state space models. The identification process is carried out from data previously sampled from the system. Unlike most existing methods that prioritize identifying the switching instants first, a new framework is proposed in which the local models are identified first, and the task of identifying the switching instants is performed later. The methodology involves the iterative calculation of discrete and continuous models’ a posteriori probability density function using subspace identification, clustering, data classification, and hybrid stochastic filtering methods. This strategy allows grouping the data most likely to have been generated by the same submodels, thus allowing the estimation of these local models. An essential feature of the algorithm is that the matrices of the different submodels are identified with the same state-space basis allowing them to be evaluated and, if necessary, combined. The performance of the identification procedure is evaluated through numerical examples, and a comparison with a prior method described in the literature is conducted.-
Descrição: dc.descriptionFaculdade UnB Gama (FGA)-
Descrição: dc.descriptionFaculdade de Tecnologia (FT)-
Descrição: dc.descriptionDepartamento de Engenharia Elétrica (FT ENE)-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationhttps://link.springer.com/article/10.1007/s40430-023-04505-2-
Direitos: dc.rightsAcesso Restrito-
Palavras-chave: dc.subjectIdentificação de sistemas-
Palavras-chave: dc.subjectSistemas lineares-
Palavras-chave: dc.subjectAgrupamento de dados-
Palavras-chave: dc.subjectFiltragem estocástica híbrida-
Título: dc.titleNew framework for identifying discrete‑time switched linear systems-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional – UNB

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