New framework for identifying discrete‑time switched linear systems

Lopes, Renato Vilela; Ishihara, João Yoshiyuki; Borges, Geovany Araújo

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Campo DC	Valor	Idioma
dc.contributor.author	Lopes, Renato Vilela	-
dc.contributor.author	Ishihara, João Yoshiyuki	-
dc.contributor.author	Borges, Geovany Araújo	-
dc.date.accessioned	2024-01-22T15:33:05Z	-
dc.date.available	2024-01-22T15:33:05Z	-
dc.date.issued	2023-11-08	-
dc.identifier.citation	LOPES, Renato V.; ISHIHARA, João Y.; BORGES, Geovany A. New framework for identifying discrete‑time switched linear systems. Journal of the Brazilian Society of Mechanical Sciences and Engineering, [S. l.], v. 45, art. n. 623, 2023. DOI: https://doi.org/10.1007/s40430-023-04505-2.	pt_BR
dc.identifier.uri	http://repositorio2.unb.br/jspui/handle/10482/47430	-
dc.language.iso	eng	pt_BR
dc.publisher	Springer	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	New framework for identifying discrete‑time switched linear systems	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Identificação de sistemas	pt_BR
dc.subject.keyword	Sistemas lineares	pt_BR
dc.subject.keyword	Agrupamento de dados	pt_BR
dc.subject.keyword	Filtragem estocástica híbrida	pt_BR
dc.identifier.doi	https://doi.org/10.1007/s40430-023-04505-2	pt_BR
dc.relation.publisherversion	https://link.springer.com/article/10.1007/s40430-023-04505-2	pt_BR
dc.description.abstract1	This 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.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-8824-6384	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-5916-0207	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-4265-9471	pt_BR
dc.contributor.affiliation	University of Brasília, Faculty of Gama	pt_BR
dc.contributor.affiliation	University of Brasília, Faculty of Technology, Department of Electrical Engineering	pt_BR
dc.contributor.affiliation	University of Brasília, Faculty of Technology, Department of Electrical Engineering	pt_BR
dc.description.unidade	Faculdade UnB Gama (FGA)	pt_BR
dc.description.unidade	Faculdade de Tecnologia (FT)	pt_BR
dc.description.unidade	Departamento de Engenharia Elétrica (FT ENE)	pt_BR
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