Use of state estimation for inferential nonlinear MPC: a case study

Autores
Biagiola, Silvina Ines; Solsona, Jorge Alberto; Figueroa, Jose Luis
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear MPC may control nonlinear processes by obtaining a linearized model of the plant, however, this approach is only valid in a limited region. In the presence of marked nonlinearities, a substantial improvement can be achieved by using the whole knowledge of the process dynamics. The use of a nonlinear model for MPC involves the knowledge of the complete state vector and the most significative perturbations in order to obtain the best performance. However, this information may not be directly available through measurement. In this paper, we propose the use of a nonlinear estimator to update the state vector and to infer the unmeasured perturbations. All the development herein presented is in the context of the control of an open-loop unstable nonlinear reactor with a measurement delay in the controlled variable.
Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Solsona, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Materia
NONLINEAR MODEL PREDICTIVE CONTROL
STATE ESTIMATION
INFERENTIAL CONTROL
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:11336/104450
Acceder
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spelling Use of state estimation for inferential nonlinear MPC: a case studyBiagiola, Silvina InesSolsona, Jorge AlbertoFigueroa, Jose LuisNONLINEAR MODEL PREDICTIVE CONTROLSTATE ESTIMATIONINFERENTIAL CONTROLhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear MPC may control nonlinear processes by obtaining a linearized model of the plant, however, this approach is only valid in a limited region. In the presence of marked nonlinearities, a substantial improvement can be achieved by using the whole knowledge of the process dynamics. The use of a nonlinear model for MPC involves the knowledge of the complete state vector and the most significative perturbations in order to obtain the best performance. However, this information may not be directly available through measurement. In this paper, we propose the use of a nonlinear estimator to update the state vector and to infer the unmeasured perturbations. All the development herein presented is in the context of the control of an open-loop unstable nonlinear reactor with a measurement delay in the controlled variable.Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Solsona, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaElsevier Science Sa2005-01-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/104450Biagiola, Silvina Ines; Solsona, Jorge Alberto; Figueroa, Jose Luis; Use of state estimation for inferential nonlinear MPC: a case study; Elsevier Science Sa; Chemical Engineering Journal; 106; 1; 28-1-2005; 13-241385-8947CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1385894704003572info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cej.2004.11.002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:20Zoai:ri.conicet.gov.ar:11336/104450instacron: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:34982025-09-03 09:53:20.587CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Use of state estimation for inferential nonlinear MPC: a case study
title Use of state estimation for inferential nonlinear MPC: a case study
spellingShingle Use of state estimation for inferential nonlinear MPC: a case study
Biagiola, Silvina Ines
NONLINEAR MODEL PREDICTIVE CONTROL
STATE ESTIMATION
INFERENTIAL CONTROL
title_short Use of state estimation for inferential nonlinear MPC: a case study
title_full Use of state estimation for inferential nonlinear MPC: a case study
title_fullStr Use of state estimation for inferential nonlinear MPC: a case study
title_full_unstemmed Use of state estimation for inferential nonlinear MPC: a case study
title_sort Use of state estimation for inferential nonlinear MPC: a case study
dc.creator.none.fl_str_mv Biagiola, Silvina Ines
Solsona, Jorge Alberto
Figueroa, Jose Luis
author Biagiola, Silvina Ines
author_facet Biagiola, Silvina Ines
Solsona, Jorge Alberto
Figueroa, Jose Luis
author_role author
author2 Solsona, Jorge Alberto
Figueroa, Jose Luis
author2_role author
author
dc.subject.none.fl_str_mv NONLINEAR MODEL PREDICTIVE CONTROL
STATE ESTIMATION
INFERENTIAL CONTROL
topic NONLINEAR MODEL PREDICTIVE CONTROL
STATE ESTIMATION
INFERENTIAL CONTROL
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear MPC may control nonlinear processes by obtaining a linearized model of the plant, however, this approach is only valid in a limited region. In the presence of marked nonlinearities, a substantial improvement can be achieved by using the whole knowledge of the process dynamics. The use of a nonlinear model for MPC involves the knowledge of the complete state vector and the most significative perturbations in order to obtain the best performance. However, this information may not be directly available through measurement. In this paper, we propose the use of a nonlinear estimator to update the state vector and to infer the unmeasured perturbations. All the development herein presented is in the context of the control of an open-loop unstable nonlinear reactor with a measurement delay in the controlled variable.
Fil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Solsona, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
description Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear MPC may control nonlinear processes by obtaining a linearized model of the plant, however, this approach is only valid in a limited region. In the presence of marked nonlinearities, a substantial improvement can be achieved by using the whole knowledge of the process dynamics. The use of a nonlinear model for MPC involves the knowledge of the complete state vector and the most significative perturbations in order to obtain the best performance. However, this information may not be directly available through measurement. In this paper, we propose the use of a nonlinear estimator to update the state vector and to infer the unmeasured perturbations. All the development herein presented is in the context of the control of an open-loop unstable nonlinear reactor with a measurement delay in the controlled variable.
publishDate 2005
dc.date.none.fl_str_mv 2005-01-28
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/11336/104450
Biagiola, Silvina Ines; Solsona, Jorge Alberto; Figueroa, Jose Luis; Use of state estimation for inferential nonlinear MPC: a case study; Elsevier Science Sa; Chemical Engineering Journal; 106; 1; 28-1-2005; 13-24
1385-8947
CONICET Digital
CONICET
url http://hdl.handle.net/11336/104450
identifier_str_mv Biagiola, Silvina Ines; Solsona, Jorge Alberto; Figueroa, Jose Luis; Use of state estimation for inferential nonlinear MPC: a case study; Elsevier Science Sa; Chemical Engineering Journal; 106; 1; 28-1-2005; 13-24
1385-8947
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1385894704003572
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cej.2004.11.002
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Sa
publisher.none.fl_str_mv Elsevier Science Sa
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 13.13397

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