Stochastic model predictive control based on Gaussian processes applied to drinking water networks

This paper focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian Processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be...

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Detalles Bibliográficos
Autores: Wang, Ye, Ocampo-Martínez, Carlos, Puig, Vicenç
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2016
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/132968
Acceso en línea:http://hdl.handle.net/10261/132968
Access Level:acceso abierto
Palabra clave:Stochastic model predictive control
Gaussian processes
Disturbance forecasting
Drinking water networks
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spelling Stochastic model predictive control based on Gaussian processes applied to drinking water networksWang, YeOcampo-Martínez, CarlosPuig, VicençStochastic model predictive controlGaussian processesDisturbance forecastingDrinking water networksThis paper focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian Processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GPs. This fact allows obtaining the confidence intervals for state evolutions over the MPC prediction horizon that are included into the MPC objective function and constraints. The feasibility of the proposed MPC strategy considering the incorporated results of disturbance forecasting is also discussed. Simulation results obtained from the application of the proposed approach to the Barcelona drinking water network (DWN) taking real demand data into account are presented. The comparison with the well-known certainty-equivalent MPC shows the effectiveness of the proposed stochastic MPC approach.This work was supported by Spanish Government (Ministerio de Economía y Competitividad) and FEDER under project DPI2014-58104-R (HARCRICS). Moreover, this paper was partially supported by the research project ECOCIS DPI-2013-48243-C2-1-R of the Spanish Ministry of Science and by AGAUR Doctorat Industrial 2013-DI-041.Peer reviewedInstitution of Engineering and TechnologyMinisterio de Economía y Competitividad (España)Ministerio de Ciencia e Innovación (España)Generalitat de CatalunyaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201620162016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/132968reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2014-58104-Rinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2013-48243-C2-1-Rhttp://dx.doi.org/10.1049/iet-cta.2015.0657Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1329682026-05-22T06:33:51Z
dc.title.none.fl_str_mv Stochastic model predictive control based on Gaussian processes applied to drinking water networks
title Stochastic model predictive control based on Gaussian processes applied to drinking water networks
spellingShingle Stochastic model predictive control based on Gaussian processes applied to drinking water networks
Wang, Ye
Stochastic model predictive control
Gaussian processes
Disturbance forecasting
Drinking water networks
title_short Stochastic model predictive control based on Gaussian processes applied to drinking water networks
title_full Stochastic model predictive control based on Gaussian processes applied to drinking water networks
title_fullStr Stochastic model predictive control based on Gaussian processes applied to drinking water networks
title_full_unstemmed Stochastic model predictive control based on Gaussian processes applied to drinking water networks
title_sort Stochastic model predictive control based on Gaussian processes applied to drinking water networks
dc.creator.none.fl_str_mv Wang, Ye
Ocampo-Martínez, Carlos
Puig, Vicenç
author Wang, Ye
author_facet Wang, Ye
Ocampo-Martínez, Carlos
Puig, Vicenç
author_role author
author2 Ocampo-Martínez, Carlos
Puig, Vicenç
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia e Innovación (España)
Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Stochastic model predictive control
Gaussian processes
Disturbance forecasting
Drinking water networks
topic Stochastic model predictive control
Gaussian processes
Disturbance forecasting
Drinking water networks
description This paper focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian Processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GPs. This fact allows obtaining the confidence intervals for state evolutions over the MPC prediction horizon that are included into the MPC objective function and constraints. The feasibility of the proposed MPC strategy considering the incorporated results of disturbance forecasting is also discussed. Simulation results obtained from the application of the proposed approach to the Barcelona drinking water network (DWN) taking real demand data into account are presented. The comparison with the well-known certainty-equivalent MPC shows the effectiveness of the proposed stochastic MPC approach.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/132968
url http://hdl.handle.net/10261/132968
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2014-58104-R
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2013-48243-C2-1-R
http://dx.doi.org/10.1049/iet-cta.2015.0657
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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