A tool to analyze robust stability for constrained nonlinear MPC

Detalhes bibliográficos
Autor(a) principal: Santos, Lino O.
Data de Publicação: 2008
Outros Autores: Biegler, Lorenz T., Castro, José A. A. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/3777
https://doi.org/10.1016/s1474-6670(17)38779-7
Resumo: A sufficient condition for robust asymptotic stability of nonlinear constrained model predictive control (MPC) is derived with respect to plant/model mismatch. This work is an extension of a previous study on the unconstrained nonlinear MPC problem, and is based on nonlinear programming sensitivity concepts. It addresses the discrete time state feedback problem with all states measured. A strategy to estimate bounds on the plant/model mismatch is proposed that can be used off-line as a tool to assess the extent of model mismatch that can be tolerated to guarantee robust stability.
id RCAP_305a782a3d5a3a388e6f6459c0381e37
oai_identifier_str oai:estudogeral.uc.pt:10316/3777
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling A tool to analyze robust stability for constrained nonlinear MPCNonlinear model predictive controlRobust stabilitySensitivity analysisA sufficient condition for robust asymptotic stability of nonlinear constrained model predictive control (MPC) is derived with respect to plant/model mismatch. This work is an extension of a previous study on the unconstrained nonlinear MPC problem, and is based on nonlinear programming sensitivity concepts. It addresses the discrete time state feedback problem with all states measured. A strategy to estimate bounds on the plant/model mismatch is proposed that can be used off-line as a tool to assess the extent of model mismatch that can be tolerated to guarantee robust stability.http://www.sciencedirect.com/science/article/B6V4N-4R7F42T-2/1/7729956156701c2970c6a488f92988452008info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleaplication/PDFhttp://hdl.handle.net/10316/3777http://hdl.handle.net/10316/3777https://doi.org/10.1016/s1474-6670(17)38779-7engJournal of Process Control. 18:3-4 (2008) 383-390Santos, Lino O.Biegler, Lorenz T.Castro, José A. A. M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-11-06T16:48:33Zoai:estudogeral.uc.pt:10316/3777Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:59:17.236442Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A tool to analyze robust stability for constrained nonlinear MPC
title A tool to analyze robust stability for constrained nonlinear MPC
spellingShingle A tool to analyze robust stability for constrained nonlinear MPC
Santos, Lino O.
Nonlinear model predictive control
Robust stability
Sensitivity analysis
title_short A tool to analyze robust stability for constrained nonlinear MPC
title_full A tool to analyze robust stability for constrained nonlinear MPC
title_fullStr A tool to analyze robust stability for constrained nonlinear MPC
title_full_unstemmed A tool to analyze robust stability for constrained nonlinear MPC
title_sort A tool to analyze robust stability for constrained nonlinear MPC
author Santos, Lino O.
author_facet Santos, Lino O.
Biegler, Lorenz T.
Castro, José A. A. M.
author_role author
author2 Biegler, Lorenz T.
Castro, José A. A. M.
author2_role author
author
dc.contributor.author.fl_str_mv Santos, Lino O.
Biegler, Lorenz T.
Castro, José A. A. M.
dc.subject.por.fl_str_mv Nonlinear model predictive control
Robust stability
Sensitivity analysis
topic Nonlinear model predictive control
Robust stability
Sensitivity analysis
description A sufficient condition for robust asymptotic stability of nonlinear constrained model predictive control (MPC) is derived with respect to plant/model mismatch. This work is an extension of a previous study on the unconstrained nonlinear MPC problem, and is based on nonlinear programming sensitivity concepts. It addresses the discrete time state feedback problem with all states measured. A strategy to estimate bounds on the plant/model mismatch is proposed that can be used off-line as a tool to assess the extent of model mismatch that can be tolerated to guarantee robust stability.
publishDate 2008
dc.date.none.fl_str_mv 2008
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/3777
http://hdl.handle.net/10316/3777
https://doi.org/10.1016/s1474-6670(17)38779-7
url http://hdl.handle.net/10316/3777
https://doi.org/10.1016/s1474-6670(17)38779-7
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Process Control. 18:3-4 (2008) 383-390
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv aplication/PDF
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799133884113223680