A robust gradient-based MPC for integrating real time optimizer (RTO) with control

A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution...

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Detalhes bibliográficos
Autores: D'jorge, Agustina, Ferramosca, Antonio, González, Alejandro Hernán
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2017
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositório:CONICET Digital (CONICET)
Idioma:inglês
OAI Identifier:oai:ri.conicet.gov.ar:11336/46943
Acesso em linha:http://hdl.handle.net/11336/46943
Access Level:Acceso aberto
Palavra-chave:Model Predictive Control
Economic Optimization
Robust Control
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
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repository_id_str
spelling A robust gradient-based MPC for integrating real time optimizer (RTO) with controlD'jorge, AgustinaFerramosca, AntonioGonzález, Alejandro HernánModel Predictive ControlEconomic OptimizationRobust Controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) problem. The main benefits of this strategy are that convergence is still guaranteed and good economic performances are obtained, according to several simulation scenarios. The formulation, however, is developed only for the nominal case, which significantly reduces its applicability. In this work, an extension of the gradient-based MPC to explicitly account for disturbances is made. The resulting robust formulation considers a nominal prediction model, but restricted constraints (in order to account for the effect of additive disturbances). The nominal economic performance is preserved and robust stability is ensured. An illustrative example shows the benefits of the proposal.Fil: D'jorge, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Ferramosca, Antonio. Universidad Tecnológica Nacional; ArgentinaFil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaElsevier2017-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/46943D'jorge, Agustina; Ferramosca, Antonio; González, Alejandro Hernán; A robust gradient-based MPC for integrating real time optimizer (RTO) with control; Elsevier; Journal Of Process Control; 54; 6-2017; 65-800959-1524CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jprocont.2017.02.015info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0959152417300410info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:41:00Zoai:ri.conicet.gov.ar:11336/46943instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 13:41:00.547CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A robust gradient-based MPC for integrating real time optimizer (RTO) with control
title A robust gradient-based MPC for integrating real time optimizer (RTO) with control
spellingShingle A robust gradient-based MPC for integrating real time optimizer (RTO) with control
D'jorge, Agustina
Model Predictive Control
Economic Optimization
Robust Control
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
title_short A robust gradient-based MPC for integrating real time optimizer (RTO) with control
title_full A robust gradient-based MPC for integrating real time optimizer (RTO) with control
title_fullStr A robust gradient-based MPC for integrating real time optimizer (RTO) with control
title_full_unstemmed A robust gradient-based MPC for integrating real time optimizer (RTO) with control
title_sort A robust gradient-based MPC for integrating real time optimizer (RTO) with control
dc.creator.none.fl_str_mv D'jorge, Agustina
Ferramosca, Antonio
González, Alejandro Hernán
author D'jorge, Agustina
author_facet D'jorge, Agustina
Ferramosca, Antonio
González, Alejandro Hernán
author_role author
author2 Ferramosca, Antonio
González, Alejandro Hernán
author2_role author
author
dc.subject.none.fl_str_mv Model Predictive Control
Economic Optimization
Robust Control
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
topic Model Predictive Control
Economic Optimization
Robust Control
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
description A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) problem. The main benefits of this strategy are that convergence is still guaranteed and good economic performances are obtained, according to several simulation scenarios. The formulation, however, is developed only for the nominal case, which significantly reduces its applicability. In this work, an extension of the gradient-based MPC to explicitly account for disturbances is made. The resulting robust formulation considers a nominal prediction model, but restricted constraints (in order to account for the effect of additive disturbances). The nominal economic performance is preserved and robust stability is ensured. An illustrative example shows the benefits of the proposal.
publishDate 2017
dc.date.none.fl_str_mv 2017-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/46943
D'jorge, Agustina; Ferramosca, Antonio; González, Alejandro Hernán; A robust gradient-based MPC for integrating real time optimizer (RTO) with control; Elsevier; Journal Of Process Control; 54; 6-2017; 65-80
0959-1524
CONICET Digital
CONICET
url http://hdl.handle.net/11336/46943
identifier_str_mv D'jorge, Agustina; Ferramosca, Antonio; González, Alejandro Hernán; A robust gradient-based MPC for integrating real time optimizer (RTO) with control; Elsevier; Journal Of Process Control; 54; 6-2017; 65-80
0959-1524
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jprocont.2017.02.015
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0959152417300410
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 15,811543

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