Online learning-based model predictive control with Gaussian process models and stability guarantees

Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challen...

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Detalhes bibliográficos
Autores: Maiworm, Michael, Limón Marruedo, Daniel, Findeisen, Rolf
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/134975
Acesso em linha:https://hdl.handle.net/11441/134975
https://doi.org/10.1002/rnc.5361
Access Level:acceso abierto
Palavra-chave:Gaussian processes
Input-to-state stability
Machine learning
Online learning
Predictive control
Recursive updates
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spelling Online learning-based model predictive control with Gaussian process models and stability guaranteesMaiworm, MichaelLimón Marruedo, DanielFindeisen, RolfGaussian processesInput-to-state stabilityMachine learningOnline learningPredictive controlRecursive updatesModel predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model–plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance.Ministerio de Economía y Competitividad ( España) DPI2016-76493-C3-1-RWilleyIngeniería de Sistemas y AutomáticaMinisterio de Economía y Competitividad (MINECO). España2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/134975https://doi.org/10.1002/rnc.5361reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Robust and Nonlinear Control, Special Issue Article, 8785-8812.DPI2016-76493-C3-1-Rhttps://onlinelibrary.wiley.com/doi/full/10.1002/rnc.5361info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1349752026-06-17T12:51:07Z
dc.title.none.fl_str_mv Online learning-based model predictive control with Gaussian process models and stability guarantees
title Online learning-based model predictive control with Gaussian process models and stability guarantees
spellingShingle Online learning-based model predictive control with Gaussian process models and stability guarantees
Maiworm, Michael
Gaussian processes
Input-to-state stability
Machine learning
Online learning
Predictive control
Recursive updates
title_short Online learning-based model predictive control with Gaussian process models and stability guarantees
title_full Online learning-based model predictive control with Gaussian process models and stability guarantees
title_fullStr Online learning-based model predictive control with Gaussian process models and stability guarantees
title_full_unstemmed Online learning-based model predictive control with Gaussian process models and stability guarantees
title_sort Online learning-based model predictive control with Gaussian process models and stability guarantees
dc.creator.none.fl_str_mv Maiworm, Michael
Limón Marruedo, Daniel
Findeisen, Rolf
author Maiworm, Michael
author_facet Maiworm, Michael
Limón Marruedo, Daniel
Findeisen, Rolf
author_role author
author2 Limón Marruedo, Daniel
Findeisen, Rolf
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Gaussian processes
Input-to-state stability
Machine learning
Online learning
Predictive control
Recursive updates
topic Gaussian processes
Input-to-state stability
Machine learning
Online learning
Predictive control
Recursive updates
description Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model–plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/134975
https://doi.org/10.1002/rnc.5361
url https://hdl.handle.net/11441/134975
https://doi.org/10.1002/rnc.5361
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Robust and Nonlinear Control, Special Issue Article, 8785-8812.
DPI2016-76493-C3-1-R
https://onlinelibrary.wiley.com/doi/full/10.1002/rnc.5361
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 Willey
publisher.none.fl_str_mv Willey
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 15,301603

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