MPC with learning properties applied to finite-horizon repetitive systems

Autores
Adam, Eduardo Jose; González, Alejandro Hernán
Año de publicación
2012
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
A repetitive system is one that continuously repeats a finite-duration procedure (operation) along the time. This kind of systems can be found in several industrial fields such as robot manipulation ((Tan, Huang, Lee & Tay, 2003)), injection molding ((Yao, Gao & Allgöwer, 2008)), batch processes ((Bonvin et al., 1984; Lee & Lee, 1999)) and semiconductor processes ((Moyne, Castillo, & Hurwitz, 2003)). Because of the repetitive characteristic, these systems have two count indexes or time scales: o e for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence. Consequently, it can be said that a control strategy for repetitive systems requires accounting for two different objectives: a short-term disturbance rejection during a finite-duration single operation in the continuous sequence (this frequently means the tracking of a predetermined optimal trajectory) and the long-term disturbance rejection from operation to operation (i.e., considering each operation as a single point of a continuous process1). The MPC proposed in this Chapter is formulated under a closed-loop paradigm ((Rossiter, 2003)). The basic idea of a closed-loop paradigm is to choose a stabilizing control law and assume that this law (underlying input sequence) is present throughout the predictions. More precisely, the MPC propose here is an Infinite Horizon MPC (IHMPC) that includes an underlying control sequence as a (deficient) reference candidate to be improved for the tracking control. Then, by solving on line a constrained optimization problem, the input sequence is corrected, and so the learning updating is performed.
Fil: Adam, Eduardo Jose. 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: 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
Materia
MODEL PREDICTIVE CONTROL
REPETITIVE SYSTEMS
LEARNING PROPERTIES
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:11336/110188
Acceder
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network_name_str CONICET Digital (CONICET)
spelling MPC with learning properties applied to finite-horizon repetitive systemsAdam, Eduardo JoseGonzález, Alejandro HernánMODEL PREDICTIVE CONTROLREPETITIVE SYSTEMSLEARNING PROPERTIEShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2A repetitive system is one that continuously repeats a finite-duration procedure (operation) along the time. This kind of systems can be found in several industrial fields such as robot manipulation ((Tan, Huang, Lee & Tay, 2003)), injection molding ((Yao, Gao & Allgöwer, 2008)), batch processes ((Bonvin et al., 1984; Lee & Lee, 1999)) and semiconductor processes ((Moyne, Castillo, & Hurwitz, 2003)). Because of the repetitive characteristic, these systems have two count indexes or time scales: o e for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence. Consequently, it can be said that a control strategy for repetitive systems requires accounting for two different objectives: a short-term disturbance rejection during a finite-duration single operation in the continuous sequence (this frequently means the tracking of a predetermined optimal trajectory) and the long-term disturbance rejection from operation to operation (i.e., considering each operation as a single point of a continuous process1). The MPC proposed in this Chapter is formulated under a closed-loop paradigm ((Rossiter, 2003)). The basic idea of a closed-loop paradigm is to choose a stabilizing control law and assume that this law (underlying input sequence) is present throughout the predictions. More precisely, the MPC propose here is an Infinite Horizon MPC (IHMPC) that includes an underlying control sequence as a (deficient) reference candidate to be improved for the tracking control. Then, by solving on line a constrained optimization problem, the input sequence is corrected, and so the learning updating is performed.Fil: Adam, Eduardo Jose. 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: 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; ArgentinaIntechOpende Oliveira Serra, Ginalber Luiz2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookParthttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/110188Adam, Eduardo Jose; González, Alejandro Hernán; MPC with learning properties applied to finite-horizon repetitive systems; IntechOpen; 2012; 193-213978-953-51-0677-7CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/frontiers-in-advanced-control-systemsinfo:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/frontiers-in-advanced-control-systems/iterative-learning-mpc-an-alternative-strategyinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:50:32Zoai:ri.conicet.gov.ar:11336/110188instacron: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 09:50:32.339CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv MPC with learning properties applied to finite-horizon repetitive systems
title MPC with learning properties applied to finite-horizon repetitive systems
spellingShingle MPC with learning properties applied to finite-horizon repetitive systems
Adam, Eduardo Jose
MODEL PREDICTIVE CONTROL
REPETITIVE SYSTEMS
LEARNING PROPERTIES
title_short MPC with learning properties applied to finite-horizon repetitive systems
title_full MPC with learning properties applied to finite-horizon repetitive systems
title_fullStr MPC with learning properties applied to finite-horizon repetitive systems
title_full_unstemmed MPC with learning properties applied to finite-horizon repetitive systems
title_sort MPC with learning properties applied to finite-horizon repetitive systems
dc.creator.none.fl_str_mv Adam, Eduardo Jose
González, Alejandro Hernán
author Adam, Eduardo Jose
author_facet Adam, Eduardo Jose
González, Alejandro Hernán
author_role author
author2 González, Alejandro Hernán
author2_role author
dc.contributor.none.fl_str_mv de Oliveira Serra, Ginalber Luiz
dc.subject.none.fl_str_mv MODEL PREDICTIVE CONTROL
REPETITIVE SYSTEMS
LEARNING PROPERTIES
topic MODEL PREDICTIVE CONTROL
REPETITIVE SYSTEMS
LEARNING PROPERTIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A repetitive system is one that continuously repeats a finite-duration procedure (operation) along the time. This kind of systems can be found in several industrial fields such as robot manipulation ((Tan, Huang, Lee & Tay, 2003)), injection molding ((Yao, Gao & Allgöwer, 2008)), batch processes ((Bonvin et al., 1984; Lee & Lee, 1999)) and semiconductor processes ((Moyne, Castillo, & Hurwitz, 2003)). Because of the repetitive characteristic, these systems have two count indexes or time scales: o e for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence. Consequently, it can be said that a control strategy for repetitive systems requires accounting for two different objectives: a short-term disturbance rejection during a finite-duration single operation in the continuous sequence (this frequently means the tracking of a predetermined optimal trajectory) and the long-term disturbance rejection from operation to operation (i.e., considering each operation as a single point of a continuous process1). The MPC proposed in this Chapter is formulated under a closed-loop paradigm ((Rossiter, 2003)). The basic idea of a closed-loop paradigm is to choose a stabilizing control law and assume that this law (underlying input sequence) is present throughout the predictions. More precisely, the MPC propose here is an Infinite Horizon MPC (IHMPC) that includes an underlying control sequence as a (deficient) reference candidate to be improved for the tracking control. Then, by solving on line a constrained optimization problem, the input sequence is corrected, and so the learning updating is performed.
Fil: Adam, Eduardo Jose. 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: 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
description A repetitive system is one that continuously repeats a finite-duration procedure (operation) along the time. This kind of systems can be found in several industrial fields such as robot manipulation ((Tan, Huang, Lee & Tay, 2003)), injection molding ((Yao, Gao & Allgöwer, 2008)), batch processes ((Bonvin et al., 1984; Lee & Lee, 1999)) and semiconductor processes ((Moyne, Castillo, & Hurwitz, 2003)). Because of the repetitive characteristic, these systems have two count indexes or time scales: o e for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence. Consequently, it can be said that a control strategy for repetitive systems requires accounting for two different objectives: a short-term disturbance rejection during a finite-duration single operation in the continuous sequence (this frequently means the tracking of a predetermined optimal trajectory) and the long-term disturbance rejection from operation to operation (i.e., considering each operation as a single point of a continuous process1). The MPC proposed in this Chapter is formulated under a closed-loop paradigm ((Rossiter, 2003)). The basic idea of a closed-loop paradigm is to choose a stabilizing control law and assume that this law (underlying input sequence) is present throughout the predictions. More precisely, the MPC propose here is an Infinite Horizon MPC (IHMPC) that includes an underlying control sequence as a (deficient) reference candidate to be improved for the tracking control. Then, by solving on line a constrained optimization problem, the input sequence is corrected, and so the learning updating is performed.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/bookPart
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
status_str publishedVersion
format bookPart
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/110188
Adam, Eduardo Jose; González, Alejandro Hernán; MPC with learning properties applied to finite-horizon repetitive systems; IntechOpen; 2012; 193-213
978-953-51-0677-7
CONICET Digital
CONICET
url http://hdl.handle.net/11336/110188
identifier_str_mv Adam, Eduardo Jose; González, Alejandro Hernán; MPC with learning properties applied to finite-horizon repetitive systems; IntechOpen; 2012; 193-213
978-953-51-0677-7
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.intechopen.com/books/frontiers-in-advanced-control-systems
info:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/frontiers-in-advanced-control-systems/iterative-learning-mpc-an-alternative-strategy
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 IntechOpen
publisher.none.fl_str_mv IntechOpen
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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