Robust learning-based MPC for nonlinear constrained systems

Sherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278

Detalles Bibliográficos
Autores: Manzano Crespo, José María, Limón Marruedo, Daniel, Muñoz de la Peña Sequedo, David, Calliess, Jan Peter
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/109011
Acceso en línea:https://hdl.handle.net/11441/109011
https://doi.org/10.1016/j.automatica.2020.108948
Access Level:acceso abierto
Palabra clave:Predictive control
Learning control
Robust stability
Nonlinear systems
Lyapunov stability
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spelling Robust learning-based MPC for nonlinear constrained systemsManzano Crespo, José MaríaLimón Marruedo, DanielMuñoz de la Peña Sequedo, DavidCalliess, Jan PeterPredictive controlLearning controlRobust stabilityNonlinear systemsLyapunov stabilitySherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study.Premio Trimestral Publicación Científica Destacada de la US. Escuela Técnica Superior de IngenieríaElsevierIngeniería de Sistemas y AutomáticaTEP950: Estimación, Predicción, Optimización y Control2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/109011https://doi.org/10.1016/j.automatica.2020.108948reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésAutomatica, 117, Art. number 108948.https://www.sciencedirect.com/science/article/pii/S0005109820301461info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1090112026-06-17T12:51:07Z
dc.title.none.fl_str_mv Robust learning-based MPC for nonlinear constrained systems
title Robust learning-based MPC for nonlinear constrained systems
spellingShingle Robust learning-based MPC for nonlinear constrained systems
Manzano Crespo, José María
Predictive control
Learning control
Robust stability
Nonlinear systems
Lyapunov stability
title_short Robust learning-based MPC for nonlinear constrained systems
title_full Robust learning-based MPC for nonlinear constrained systems
title_fullStr Robust learning-based MPC for nonlinear constrained systems
title_full_unstemmed Robust learning-based MPC for nonlinear constrained systems
title_sort Robust learning-based MPC for nonlinear constrained systems
dc.creator.none.fl_str_mv Manzano Crespo, José María
Limón Marruedo, Daniel
Muñoz de la Peña Sequedo, David
Calliess, Jan Peter
author Manzano Crespo, José María
author_facet Manzano Crespo, José María
Limón Marruedo, Daniel
Muñoz de la Peña Sequedo, David
Calliess, Jan Peter
author_role author
author2 Limón Marruedo, Daniel
Muñoz de la Peña Sequedo, David
Calliess, Jan Peter
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
TEP950: Estimación, Predicción, Optimización y Control
dc.subject.none.fl_str_mv Predictive control
Learning control
Robust stability
Nonlinear systems
Lyapunov stability
topic Predictive control
Learning control
Robust stability
Nonlinear systems
Lyapunov stability
description Sherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/109011
https://doi.org/10.1016/j.automatica.2020.108948
url https://hdl.handle.net/11441/109011
https://doi.org/10.1016/j.automatica.2020.108948
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Automatica, 117, Art. number 108948.
https://www.sciencedirect.com/science/article/pii/S0005109820301461
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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