Least-squares parameter estimation for statespace models with state equality constraints.

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dc.contributor.authorRicco, Rodrigo Augusto
dc.contributor.authorTeixeira, Bruno Otávio Soares
dc.date.accessioned2022-03-08T18:43:39Z
dc.date.available2022-03-08T18:43:39Z
dc.date.issued2021pt_BR
dc.description.abstractIf a dynamic system has active constraints on the state vector and they are known, then taking them into account during modeling is often advantageous. Unfortunately, in the constrained discrete-time state-space estimation, the state equality constraint is defined for a parameter matrix and not on a parameter vector as commonly found in regression problems. To address this problem, firstly, we show how to rewrite the state equality constraints as equality constraints on the state matrices to be estimated. Then, we vectorise the matricial least squares problem defined for modeling statespace systems such that any method from the equality-constrained least squares framework may be employed. Both time-invariant and time-varying cases are considered as well as the case where the state equality constraint is not exactly known.pt_BR
dc.identifier.citationRICCO, R. A.; TEIXEIRA, B. O. S. Least-squares parameter estimation for statespace models with state equality constraints. International Journal of Systems Science, v. 53, n. 1, jun. 2021. Disponível em: <https://www.tandfonline.com/doi/abs/10.1080/00207721.2021.1936273>. Acesso em: 12 set. 2021.pt_BR
dc.identifier.doihttps://doi.org/10.1080/00207721.2021.1936273pt_BR
dc.identifier.issn1464-5319
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/14636
dc.identifier.uri2https://www.tandfonline.com/doi/abs/10.1080/00207721.2021.1936273pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectState equality constraintspt_BR
dc.subjectState-space modelingpt_BR
dc.subjectGray-box modelingpt_BR
dc.subjectConstrained estimationpt_BR
dc.titleLeast-squares parameter estimation for statespace models with state equality constraints.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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