Model-free learning control of neutralization processes using reinforcement learning

Autores
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This "on-the-fly" learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.
Fil: Syafiie, S.. Universidad de Valladolid; España
Fil: Tadeo, F.. Universidad de Valladolid; España
Fil: Martínez, 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
Learning Control
Reinforcement Learning
Ph Control
Model-Free 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:11336/83738
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Model-free learning control of neutralization processes using reinforcement learningSyafiie, S.Tadeo, F.Martínez, Ernesto CarlosLearning ControlReinforcement LearningPh ControlModel-Free Controlhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This "on-the-fly" learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.Fil: Syafiie, S.. Universidad de Valladolid; EspañaFil: Tadeo, F.. Universidad de Valladolid; EspañaFil: Martínez, 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; ArgentinaPergamon-Elsevier Science Ltd2007-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83738Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Model-free learning control of neutralization processes using reinforcement learning; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 20; 6; 9-2007; 767-7820952-1976CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2006.10.009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:39:10Zoai:ri.conicet.gov.ar:11336/83738instacron: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 09:39:10.882CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model-free learning control of neutralization processes using reinforcement learning
title Model-free learning control of neutralization processes using reinforcement learning
spellingShingle Model-free learning control of neutralization processes using reinforcement learning
Syafiie, S.
Learning Control
Reinforcement Learning
Ph Control
Model-Free Control
title_short Model-free learning control of neutralization processes using reinforcement learning
title_full Model-free learning control of neutralization processes using reinforcement learning
title_fullStr Model-free learning control of neutralization processes using reinforcement learning
title_full_unstemmed Model-free learning control of neutralization processes using reinforcement learning
title_sort Model-free learning control of neutralization processes using reinforcement learning
dc.creator.none.fl_str_mv Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
author Syafiie, S.
author_facet Syafiie, S.
Tadeo, F.
Martínez, Ernesto Carlos
author_role author
author2 Tadeo, F.
Martínez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv Learning Control
Reinforcement Learning
Ph Control
Model-Free Control
topic Learning Control
Reinforcement Learning
Ph Control
Model-Free Control
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This "on-the-fly" learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.
Fil: Syafiie, S.. Universidad de Valladolid; España
Fil: Tadeo, F.. Universidad de Valladolid; España
Fil: Martínez, 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 The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This "on-the-fly" learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.
publishDate 2007
dc.date.none.fl_str_mv 2007-09
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/11336/83738
Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Model-free learning control of neutralization processes using reinforcement learning; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 20; 6; 9-2007; 767-782
0952-1976
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83738
identifier_str_mv Syafiie, S.; Tadeo, F.; Martínez, Ernesto Carlos; Model-free learning control of neutralization processes using reinforcement learning; Pergamon-Elsevier Science Ltd; Engineering Applications Of Artificial Intelligence; 20; 6; 9-2007; 767-782
0952-1976
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.engappai.2006.10.009
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 Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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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