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

This study 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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Detalhes bibliográficos
Autores: Wang, Ye|||0000-0003-1395-1676, Ocampo-Martínez, Carlos|||0000-0001-9251-6044, Puig Cayuela, Vicenç|||0000-0002-6364-6429
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/89276
Acesso em linha:https://hdl.handle.net/2117/89276
https://dx.doi.org/10.1049/iet-cta.2015.0657
Access Level:acceso abierto
Palavra-chave:automation
control theory
optimisation
stochastic model predictive control
Gaussian processes
disturbance forecasting
drinking water networks
Classificació INSPEC::Control theory
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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oai_identifier_str oai:upcommons.upc.edu:2117/89276
network_acronym_str ES
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repository_id_str
spelling Stochastic model predictive control based on Gaussian processes applied to drinking water networksWang, Ye|||0000-0003-1395-1676Ocampo-Martínez, Carlos|||0000-0001-9251-6044Puig Cayuela, Vicenç|||0000-0002-6364-6429automationcontrol theoryoptimisationstochastic model predictive controlGaussian processesdisturbance forecastingdrinking water networksClassificació INSPEC::Control theoryÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThis study 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 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.Peer Reviewed20162016-05-1620162016-07-27journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/89276https://dx.doi.org/10.1049/iet-cta.2015.0657reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2013-48243-C2-1-R OPERACION EFICIENTE DE INFRAESTRUCTURAS CRITICASMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-58104-R CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOSMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-58104-R CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOSopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/892762026-05-27T15:37:01Z
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|||0000-0003-1395-1676
automation
control theory
optimisation
stochastic model predictive control
Gaussian processes
disturbance forecasting
drinking water networks
Classificació INSPEC::Control theory
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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|||0000-0003-1395-1676
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
author Wang, Ye|||0000-0003-1395-1676
author_facet Wang, Ye|||0000-0003-1395-1676
Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
author_role author
author2 Ocampo-Martínez, Carlos|||0000-0001-9251-6044
Puig Cayuela, Vicenç|||0000-0002-6364-6429
author2_role author
author
dc.subject.none.fl_str_mv automation
control theory
optimisation
stochastic model predictive control
Gaussian processes
disturbance forecasting
drinking water networks
Classificació INSPEC::Control theory
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic automation
control theory
optimisation
stochastic model predictive control
Gaussian processes
disturbance forecasting
drinking water networks
Classificació INSPEC::Control theory
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description This study 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 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
2016-05-16
2016
2016-07-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/89276
https://dx.doi.org/10.1049/iet-cta.2015.0657
url https://hdl.handle.net/2117/89276
https://dx.doi.org/10.1049/iet-cta.2015.0657
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2013-48243-C2-1-R OPERACION EFICIENTE DE INFRAESTRUCTURAS CRITICAS
Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-58104-R CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS
Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-58104-R CONTROL BASADO EN LA SALUD Y LA RESILIENCIA DE INFRAESTRUCTURAS CRITICAS Y SISTEMAS COMPLEJOS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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