Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty

Autores
de Paula, Mariano; Martinez, Ernesto Carlos
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combinesdynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study.
Fil: de Paula, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Optimal Operation
Discretely Controlled Continuous Systems
Reinforcement Learning
Uncertainty
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:11336/273867
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Optimal Operation of Discretely Controlled Continuous Systems under Uncertaintyde Paula, MarianoMartinez, Ernesto CarlosOptimal OperationDiscretely Controlled Continuous SystemsReinforcement LearningUncertaintyhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combinesdynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study.Fil: de Paula, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaAmerican Chemical Society2012-10info: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/11336/273867de Paula, Mariano; Martinez, Ernesto Carlos; Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty; American Chemical Society; Industrial & Engineering Chemical Research; 51; 42; 10-2012; 13743-137640888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie301015zinfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie301015zinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:40:09Zoai:ri.conicet.gov.ar:11336/273867instacron: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:34982025-11-12 09:40:09.36CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
title Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
spellingShingle Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
de Paula, Mariano
Optimal Operation
Discretely Controlled Continuous Systems
Reinforcement Learning
Uncertainty
title_short Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
title_full Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
title_fullStr Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
title_full_unstemmed Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
title_sort Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty
dc.creator.none.fl_str_mv de Paula, Mariano
Martinez, Ernesto Carlos
author de Paula, Mariano
author_facet de Paula, Mariano
Martinez, Ernesto Carlos
author_role author
author2 Martinez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Optimal Operation
Discretely Controlled Continuous Systems
Reinforcement Learning
Uncertainty
topic Optimal Operation
Discretely Controlled Continuous Systems
Reinforcement Learning
Uncertainty
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combinesdynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study.
Fil: de Paula, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parametrized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls, or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm that combinesdynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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/11336/273867
de Paula, Mariano; Martinez, Ernesto Carlos; Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty; American Chemical Society; Industrial & Engineering Chemical Research; 51; 42; 10-2012; 13743-13764
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/273867
identifier_str_mv de Paula, Mariano; Martinez, Ernesto Carlos; Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty; American Chemical Society; Industrial & Engineering Chemical Research; 51; 42; 10-2012; 13743-13764
0888-5885
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie301015z
info:eu-repo/semantics/altIdentifier/doi/10.1021/ie301015z
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 American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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 12.976206

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