A gradient-based strategy for the one-layer RTO+MPC controller

Autores
Alamo, Teodoro; Ferramosca, Antonio; González, Alejandro Hernán; Limón, Daniel; Odloak, Darci
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economicobjective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimalsetpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachabilityof those setpoints, due to the different models employed by the RTO and the MPC, as well as their dif-ferent time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamiccontrol layer is presented. To overcome the complexity of this one-layer formulation a gradient-basedapproximation is proposed, which provides a low-computational-cost suboptimal solution.
Fil: Alamo, Teodoro. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España
Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España
Fil: Odloak, Darci. Universidade de Sao Paulo; Brasil
Materia
Model Predictive Control
Real Time Optimization
Economic Objectives
Stability
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/9246
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A gradient-based strategy for the one-layer RTO+MPC controllerAlamo, TeodoroFerramosca, AntonioGonzález, Alejandro HernánLimón, DanielOdloak, DarciModel Predictive ControlReal Time OptimizationEconomic ObjectivesStabilityhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economicobjective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimalsetpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachabilityof those setpoints, due to the different models employed by the RTO and the MPC, as well as their dif-ferent time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamiccontrol layer is presented. To overcome the complexity of this one-layer formulation a gradient-basedapproximation is proposed, which provides a low-computational-cost suboptimal solution.Fil: Alamo, Teodoro. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; EspañaFil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); ArgentinaFil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; EspañaFil: Odloak, Darci. Universidade de Sao Paulo; BrasilElsevier2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/9246Alamo, Teodoro; Ferramosca, Antonio; González, Alejandro Hernán; Limón, Daniel; Odloak, Darci; A gradient-based strategy for the one-layer RTO+MPC controller; Elsevier; Journal Of Process Control; 24; 4; 1-2014; 435-4470959-1524enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jprocont.2014.02.018info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0959152414000717info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:13:18Zoai:ri.conicet.gov.ar:11336/9246instacron: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:34982025-09-29 10:13:18.968CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A gradient-based strategy for the one-layer RTO+MPC controller
title A gradient-based strategy for the one-layer RTO+MPC controller
spellingShingle A gradient-based strategy for the one-layer RTO+MPC controller
Alamo, Teodoro
Model Predictive Control
Real Time Optimization
Economic Objectives
Stability
title_short A gradient-based strategy for the one-layer RTO+MPC controller
title_full A gradient-based strategy for the one-layer RTO+MPC controller
title_fullStr A gradient-based strategy for the one-layer RTO+MPC controller
title_full_unstemmed A gradient-based strategy for the one-layer RTO+MPC controller
title_sort A gradient-based strategy for the one-layer RTO+MPC controller
dc.creator.none.fl_str_mv Alamo, Teodoro
Ferramosca, Antonio
González, Alejandro Hernán
Limón, Daniel
Odloak, Darci
author Alamo, Teodoro
author_facet Alamo, Teodoro
Ferramosca, Antonio
González, Alejandro Hernán
Limón, Daniel
Odloak, Darci
author_role author
author2 Ferramosca, Antonio
González, Alejandro Hernán
Limón, Daniel
Odloak, Darci
author2_role author
author
author
author
dc.subject.none.fl_str_mv Model Predictive Control
Real Time Optimization
Economic Objectives
Stability
topic Model Predictive Control
Real Time Optimization
Economic Objectives
Stability
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economicobjective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimalsetpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachabilityof those setpoints, due to the different models employed by the RTO and the MPC, as well as their dif-ferent time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamiccontrol layer is presented. To overcome the complexity of this one-layer formulation a gradient-basedapproximation is proposed, which provides a low-computational-cost suboptimal solution.
Fil: Alamo, Teodoro. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España
Fil: Ferramosca, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina
Fil: González, Alejandro Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Limón, Daniel. Universidad de Sevilla. Departamento de Ingenieria de Sistemas y Automatica; España
Fil: Odloak, Darci. Universidade de Sao Paulo; Brasil
description In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economicobjective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimalsetpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachabilityof those setpoints, due to the different models employed by the RTO and the MPC, as well as their dif-ferent time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamiccontrol layer is presented. To overcome the complexity of this one-layer formulation a gradient-basedapproximation is proposed, which provides a low-computational-cost suboptimal solution.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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/11336/9246
Alamo, Teodoro; Ferramosca, Antonio; González, Alejandro Hernán; Limón, Daniel; Odloak, Darci; A gradient-based strategy for the one-layer RTO+MPC controller; Elsevier; Journal Of Process Control; 24; 4; 1-2014; 435-447
0959-1524
url http://hdl.handle.net/11336/9246
identifier_str_mv Alamo, Teodoro; Ferramosca, Antonio; González, Alejandro Hernán; Limón, Daniel; Odloak, Darci; A gradient-based strategy for the one-layer RTO+MPC controller; Elsevier; Journal Of Process Control; 24; 4; 1-2014; 435-447
0959-1524
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.2014.02.018
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0959152414000717
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
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
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
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 13.070432

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