Modifier Adaptation as a Feedback Control Scheme

Autores
Marchetti, Alejandro Gabriel; de Avila Ferreira, T.; Costello, Sergio Gustavo; Bonvin, Dominique
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: de Avila Ferreira, T.. Ecole Polytechnique Federale de Lausanne; Francia
Fil: Costello, Sergio Gustavo. Ecole Polytechnique Federale de Lausanne; Francia
Fil: Bonvin, Dominique. Ecole Polytechnique Federale de Lausanne; Francia
Materia
REAL-TIME OPTIMIZATION
PLANT-MODEL MISMATCH
CONSTRAINT ADAPTATION
MODIFIER ADAPTATION
MODEL ACCURACY
MODEL ADEQUACY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/141921
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Modifier Adaptation as a Feedback Control SchemeMarchetti, Alejandro Gabrielde Avila Ferreira, T.Costello, Sergio GustavoBonvin, DominiqueREAL-TIME OPTIMIZATIONPLANT-MODEL MISMATCHCONSTRAINT ADAPTATIONMODIFIER ADAPTATIONMODEL ACCURACYMODEL ADEQUACYhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: de Avila Ferreira, T.. Ecole Polytechnique Federale de Lausanne; FranciaFil: Costello, Sergio Gustavo. Ecole Polytechnique Federale de Lausanne; FranciaFil: Bonvin, Dominique. Ecole Polytechnique Federale de Lausanne; FranciaAmerican Chemical Society2020-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/141921Marchetti, Alejandro Gabriel; de Avila Ferreira, T.; Costello, Sergio Gustavo; Bonvin, Dominique; Modifier Adaptation as a Feedback Control Scheme; American Chemical Society; Industrial & Engineering Chemical Research; 59; 6; 2-2020; 2261-22740888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b04501info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.9b04501info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:56:47Zoai:ri.conicet.gov.ar:11336/141921instacron: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 09:56:47.275CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modifier Adaptation as a Feedback Control Scheme
title Modifier Adaptation as a Feedback Control Scheme
spellingShingle Modifier Adaptation as a Feedback Control Scheme
Marchetti, Alejandro Gabriel
REAL-TIME OPTIMIZATION
PLANT-MODEL MISMATCH
CONSTRAINT ADAPTATION
MODIFIER ADAPTATION
MODEL ACCURACY
MODEL ADEQUACY
title_short Modifier Adaptation as a Feedback Control Scheme
title_full Modifier Adaptation as a Feedback Control Scheme
title_fullStr Modifier Adaptation as a Feedback Control Scheme
title_full_unstemmed Modifier Adaptation as a Feedback Control Scheme
title_sort Modifier Adaptation as a Feedback Control Scheme
dc.creator.none.fl_str_mv Marchetti, Alejandro Gabriel
de Avila Ferreira, T.
Costello, Sergio Gustavo
Bonvin, Dominique
author Marchetti, Alejandro Gabriel
author_facet Marchetti, Alejandro Gabriel
de Avila Ferreira, T.
Costello, Sergio Gustavo
Bonvin, Dominique
author_role author
author2 de Avila Ferreira, T.
Costello, Sergio Gustavo
Bonvin, Dominique
author2_role author
author
author
dc.subject.none.fl_str_mv REAL-TIME OPTIMIZATION
PLANT-MODEL MISMATCH
CONSTRAINT ADAPTATION
MODIFIER ADAPTATION
MODEL ACCURACY
MODEL ADEQUACY
topic REAL-TIME OPTIMIZATION
PLANT-MODEL MISMATCH
CONSTRAINT ADAPTATION
MODIFIER ADAPTATION
MODEL ACCURACY
MODEL ADEQUACY
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: de Avila Ferreira, T.. Ecole Polytechnique Federale de Lausanne; Francia
Fil: Costello, Sergio Gustavo. Ecole Polytechnique Federale de Lausanne; Francia
Fil: Bonvin, Dominique. Ecole Polytechnique Federale de Lausanne; Francia
description As a real-time optimization technique, modifier adaptation (MA) has gained much significance in recent years. This is mainly due to the fact that MA can deal explicitly with structural plant-model mismatch and unknown disturbances. MA is an iterative technique that is ideally suited to real-life applications. Its two main features are the way measurements are used to correct the model and the role played by the model in actually computing the next inputs. This paper analyzes these two features and shows that, although MA computes the next inputs via numerical optimization, it can be viewed as a feedback control scheme, that is, optimization implements tracking of the plant Karush-Kuhn-Tucker (KKT) conditions. As a result, the role of the model is downplayed to the point that model accuracy is not an important issue. The key issues are gradient estimation and model adequacy, the latter requiring that the model possesses the correct curvature of the cost function at the plant optimum. The main role of optimization is to identify the proper set of controlled variables (the active constraints and reduced gradients) as these might change with the operating point and disturbances. Thanks to this reduced requirement on model accuracy, MA is ideally suited to drive real-life processes to optimality. This is illustrated through two experimental systems with very different optimization features, namely, a commercial fuel-cell system and an experimental kite setup for harnessing wind energy.
publishDate 2020
dc.date.none.fl_str_mv 2020-02
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/11336/141921
Marchetti, Alejandro Gabriel; de Avila Ferreira, T.; Costello, Sergio Gustavo; Bonvin, Dominique; Modifier Adaptation as a Feedback Control Scheme; American Chemical Society; Industrial & Engineering Chemical Research; 59; 6; 2-2020; 2261-2274
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/141921
identifier_str_mv Marchetti, Alejandro Gabriel; de Avila Ferreira, T.; Costello, Sergio Gustavo; Bonvin, Dominique; Modifier Adaptation as a Feedback Control Scheme; American Chemical Society; Industrial & Engineering Chemical Research; 59; 6; 2-2020; 2261-2274
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/https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b04501
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.9b04501
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv 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
_version_ 1842269423824535552
score 13.13397

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