Robust MPC for tracking zone regions based on nominal predictions

Autores
Ferramosca, Antonio; Limon, D.; González, Alejandro Hernán; Alvarado, I.; Camacho, E. F.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper deals with the problem of robust tracking of target sets using a model predictive control (MPC) law. Real industries applications often require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some others variables – possibly including input variables – are steered to fixed target or setpoint. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. This problem is particularly interesting in case of additive disturbances which might push the outputs out of the zones. In this work, a stable robust MPC formulation for constrained linear systems, based on nominal predictions is presented. The main features of this controller are the use of nominal predictions, restricted constraints and the concept of distance from a point to a set as offset cost function. The controller ensures both recursive feasibility and local optimality. The properties of the controller are shown in a simulation test, in which we consider a subsystem of an industrial FCC system.
Fil: Ferramosca, Antonio. 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; Argentina
Fil: Limon, D.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
Fil: 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; Argentina
Fil: Alvarado, I.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
Fil: Camacho, E. F.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
Materia
Model Predictive Control
Restricted Constraints
Zone Control
Target Set Tracking
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/24431
Acceder
id CONICETDig_000dafbaf4a4ec8a78ea8f742bbcae5b
oai_identifier_str oai:ri.conicet.gov.ar:11336/24431
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust MPC for tracking zone regions based on nominal predictionsFerramosca, AntonioLimon, D.González, Alejandro HernánAlvarado, I.Camacho, E. F.Model Predictive ControlRestricted ConstraintsZone ControlTarget Set Trackinghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper deals with the problem of robust tracking of target sets using a model predictive control (MPC) law. Real industries applications often require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some others variables – possibly including input variables – are steered to fixed target or setpoint. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. This problem is particularly interesting in case of additive disturbances which might push the outputs out of the zones. In this work, a stable robust MPC formulation for constrained linear systems, based on nominal predictions is presented. The main features of this controller are the use of nominal predictions, restricted constraints and the concept of distance from a point to a set as offset cost function. The controller ensures both recursive feasibility and local optimality. The properties of the controller are shown in a simulation test, in which we consider a subsystem of an industrial FCC system.Fil: Ferramosca, Antonio. 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: Limon, D.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; EspañaFil: 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; ArgentinaFil: Alvarado, I.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; EspañaFil: Camacho, E. F.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; EspañaElsevier2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/24431Ferramosca, Antonio; Limon, D.; González, Alejandro Hernán; Alvarado, I.; Camacho, E. F.; Robust MPC for tracking zone regions based on nominal predictions; Elsevier; Journal Of Process Control; 22; 10; 12-2012; 1966-19740959-1524CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jprocont.2012.08.013info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0959152412002077info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:34Zoai:ri.conicet.gov.ar:11336/24431instacron: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 10:17:34.561CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust MPC for tracking zone regions based on nominal predictions
title Robust MPC for tracking zone regions based on nominal predictions
spellingShingle Robust MPC for tracking zone regions based on nominal predictions
Ferramosca, Antonio
Model Predictive Control
Restricted Constraints
Zone Control
Target Set Tracking
title_short Robust MPC for tracking zone regions based on nominal predictions
title_full Robust MPC for tracking zone regions based on nominal predictions
title_fullStr Robust MPC for tracking zone regions based on nominal predictions
title_full_unstemmed Robust MPC for tracking zone regions based on nominal predictions
title_sort Robust MPC for tracking zone regions based on nominal predictions
dc.creator.none.fl_str_mv Ferramosca, Antonio
Limon, D.
González, Alejandro Hernán
Alvarado, I.
Camacho, E. F.
author Ferramosca, Antonio
author_facet Ferramosca, Antonio
Limon, D.
González, Alejandro Hernán
Alvarado, I.
Camacho, E. F.
author_role author
author2 Limon, D.
González, Alejandro Hernán
Alvarado, I.
Camacho, E. F.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Model Predictive Control
Restricted Constraints
Zone Control
Target Set Tracking
topic Model Predictive Control
Restricted Constraints
Zone Control
Target Set Tracking
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper deals with the problem of robust tracking of target sets using a model predictive control (MPC) law. Real industries applications often require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some others variables – possibly including input variables – are steered to fixed target or setpoint. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. This problem is particularly interesting in case of additive disturbances which might push the outputs out of the zones. In this work, a stable robust MPC formulation for constrained linear systems, based on nominal predictions is presented. The main features of this controller are the use of nominal predictions, restricted constraints and the concept of distance from a point to a set as offset cost function. The controller ensures both recursive feasibility and local optimality. The properties of the controller are shown in a simulation test, in which we consider a subsystem of an industrial FCC system.
Fil: Ferramosca, Antonio. 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; Argentina
Fil: Limon, D.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
Fil: 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; Argentina
Fil: Alvarado, I.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
Fil: Camacho, E. F.. Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática. Escuela Superior de Ingenieros Industriales; España
description This paper deals with the problem of robust tracking of target sets using a model predictive control (MPC) law. Real industries applications often require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some others variables – possibly including input variables – are steered to fixed target or setpoint. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. This problem is particularly interesting in case of additive disturbances which might push the outputs out of the zones. In this work, a stable robust MPC formulation for constrained linear systems, based on nominal predictions is presented. The main features of this controller are the use of nominal predictions, restricted constraints and the concept of distance from a point to a set as offset cost function. The controller ensures both recursive feasibility and local optimality. The properties of the controller are shown in a simulation test, in which we consider a subsystem of an industrial FCC system.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
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/11336/24431
Ferramosca, Antonio; Limon, D.; González, Alejandro Hernán; Alvarado, I.; Camacho, E. F.; Robust MPC for tracking zone regions based on nominal predictions; Elsevier; Journal Of Process Control; 22; 10; 12-2012; 1966-1974
0959-1524
CONICET Digital
CONICET
url http://hdl.handle.net/11336/24431
identifier_str_mv Ferramosca, Antonio; Limon, D.; González, Alejandro Hernán; Alvarado, I.; Camacho, E. F.; Robust MPC for tracking zone regions based on nominal predictions; Elsevier; Journal Of Process Control; 22; 10; 12-2012; 1966-1974
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.2012.08.013
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0959152412002077
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
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
_version_ 1844614129132765184
score 13.070432

Publicaciones similares