Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets

Autores
Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; D'jorge, Agustina; Kofman, Ernesto Javier
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work extends a recent set-based Model Predictive Control (MPC) scheme for closed loop re-identification that solves the potential conflict between the simultaneous persistent excitation of the system and the stabilization of the closed-loop system. Based on the original scheme proposed in González et al. (2014), this manuscript extends those results by taking into account model uncertainties and by exploiting the knowledge of the probability distribution of the excitation signal used to identify the plant. The robust extension solves the main drawback of the previous work, which was limited to a nominal analysis while the need of re-identificationassumes the presence of model uncertainties. In addition, the probabilistic analysis allows the use of smaller target sets computed as Probabilistic Invariant Sets (PIS), improving the system performance during the identification procedure. Simulation results show the practical benefits of the novel robust strategy.
Fil: Anderson, Alejandro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional. Facultad Regional Reconquista; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: D'jorge, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Kofman, Ernesto Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Materia
Closed-Loop Re-Identification
Model Predictive Control
Probabilistic Invariant Sets
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/83081
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/83081
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant setsAnderson, Alejandro LuisGonzález, Alejandro HernánFerramosca, AntonioD'jorge, AgustinaKofman, Ernesto JavierClosed-Loop Re-IdentificationModel Predictive ControlProbabilistic Invariant Setshttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2This work extends a recent set-based Model Predictive Control (MPC) scheme for closed loop re-identification that solves the potential conflict between the simultaneous persistent excitation of the system and the stabilization of the closed-loop system. Based on the original scheme proposed in González et al. (2014), this manuscript extends those results by taking into account model uncertainties and by exploiting the knowledge of the probability distribution of the excitation signal used to identify the plant. The robust extension solves the main drawback of the previous work, which was limited to a nominal analysis while the need of re-identificationassumes the presence of model uncertainties. In addition, the probabilistic analysis allows the use of smaller target sets computed as Probabilistic Invariant Sets (PIS), improving the system performance during the identification procedure. Simulation results show the practical benefits of the novel robust strategy.Fil: Anderson, Alejandro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Ferramosca, Antonio. Universidad Tecnológica Nacional. Facultad Regional Reconquista; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: D'jorge, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Kofman, Ernesto Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaElsevier Science2018-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83081Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; D'jorge, Agustina; Kofman, Ernesto Javier; Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets; Elsevier Science; Systems And Control Letters; 118; 8-2018; 84-930167-6911CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.sysconle.2018.06.002info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167691118301099info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:49Zoai:ri.conicet.gov.ar:11336/83081instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:05:49.379CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
title Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
spellingShingle Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
Anderson, Alejandro Luis
Closed-Loop Re-Identification
Model Predictive Control
Probabilistic Invariant Sets
title_short Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
title_full Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
title_fullStr Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
title_full_unstemmed Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
title_sort Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets
dc.creator.none.fl_str_mv Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
D'jorge, Agustina
Kofman, Ernesto Javier
author Anderson, Alejandro Luis
author_facet Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
D'jorge, Agustina
Kofman, Ernesto Javier
author_role author
author2 González, Alejandro Hernán
Ferramosca, Antonio
D'jorge, Agustina
Kofman, Ernesto Javier
author2_role author
author
author
author
dc.subject.none.fl_str_mv Closed-Loop Re-Identification
Model Predictive Control
Probabilistic Invariant Sets
topic Closed-Loop Re-Identification
Model Predictive Control
Probabilistic Invariant Sets
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This work extends a recent set-based Model Predictive Control (MPC) scheme for closed loop re-identification that solves the potential conflict between the simultaneous persistent excitation of the system and the stabilization of the closed-loop system. Based on the original scheme proposed in González et al. (2014), this manuscript extends those results by taking into account model uncertainties and by exploiting the knowledge of the probability distribution of the excitation signal used to identify the plant. The robust extension solves the main drawback of the previous work, which was limited to a nominal analysis while the need of re-identificationassumes the presence of model uncertainties. In addition, the probabilistic analysis allows the use of smaller target sets computed as Probabilistic Invariant Sets (PIS), improving the system performance during the identification procedure. Simulation results show the practical benefits of the novel robust strategy.
Fil: Anderson, Alejandro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional. Facultad Regional Reconquista; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: D'jorge, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Kofman, Ernesto Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
description This work extends a recent set-based Model Predictive Control (MPC) scheme for closed loop re-identification that solves the potential conflict between the simultaneous persistent excitation of the system and the stabilization of the closed-loop system. Based on the original scheme proposed in González et al. (2014), this manuscript extends those results by taking into account model uncertainties and by exploiting the knowledge of the probability distribution of the excitation signal used to identify the plant. The robust extension solves the main drawback of the previous work, which was limited to a nominal analysis while the need of re-identificationassumes the presence of model uncertainties. In addition, the probabilistic analysis allows the use of smaller target sets computed as Probabilistic Invariant Sets (PIS), improving the system performance during the identification procedure. Simulation results show the practical benefits of the novel robust strategy.
publishDate 2018
dc.date.none.fl_str_mv 2018-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/83081
Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; D'jorge, Agustina; Kofman, Ernesto Javier; Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets; Elsevier Science; Systems And Control Letters; 118; 8-2018; 84-93
0167-6911
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83081
identifier_str_mv Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; D'jorge, Agustina; Kofman, Ernesto Javier; Robust MPC suitable for closed-loop re-identification, based on probabilistic invariant sets; Elsevier Science; Systems And Control Letters; 118; 8-2018; 84-93
0167-6911
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.sysconle.2018.06.002
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167691118301099
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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