Robust adaptive control using multiple models, switching and tuning

Autores
Giovanini, Leonardo Luis
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models PL(k), from the set of admissible models PL, that explains the input – output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on PL, allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
Materia
Robust Control
Adaptive Control
Closed Loop Systems
Control System Sysnthesis
Linear Programming
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:11336/14238
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/14238
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust adaptive control using multiple models, switching and tuningGiovanini, Leonardo LuisRobust ControlAdaptive ControlClosed Loop SystemsControl System SysnthesisLinear Programminghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models PL(k), from the set of admissible models PL, that explains the input – output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on PL, allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaInst Engineering Technology-iet2011-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14238Giovanini, Leonardo Luis; Robust adaptive control using multiple models, switching and tuning; Inst Engineering Technology-iet; Iet Control Theory And Applications; 5; 18; 12-2011; 2168-21781751-86441751-8652enginfo:eu-repo/semantics/altIdentifier/doi//10.1049/iet-cta.2010.0550info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:19:17Zoai:ri.conicet.gov.ar:11336/14238instacron: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:34982025-10-22 12:19:17.737CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust adaptive control using multiple models, switching and tuning
title Robust adaptive control using multiple models, switching and tuning
spellingShingle Robust adaptive control using multiple models, switching and tuning
Giovanini, Leonardo Luis
Robust Control
Adaptive Control
Closed Loop Systems
Control System Sysnthesis
Linear Programming
title_short Robust adaptive control using multiple models, switching and tuning
title_full Robust adaptive control using multiple models, switching and tuning
title_fullStr Robust adaptive control using multiple models, switching and tuning
title_full_unstemmed Robust adaptive control using multiple models, switching and tuning
title_sort Robust adaptive control using multiple models, switching and tuning
dc.creator.none.fl_str_mv Giovanini, Leonardo Luis
author Giovanini, Leonardo Luis
author_facet Giovanini, Leonardo Luis
author_role author
dc.subject.none.fl_str_mv Robust Control
Adaptive Control
Closed Loop Systems
Control System Sysnthesis
Linear Programming
topic Robust Control
Adaptive Control
Closed Loop Systems
Control System Sysnthesis
Linear Programming
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models PL(k), from the set of admissible models PL, that explains the input – output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on PL, allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina
description The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models PL(k), from the set of admissible models PL, that explains the input – output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on PL, allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.
publishDate 2011
dc.date.none.fl_str_mv 2011-12
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/11336/14238
Giovanini, Leonardo Luis; Robust adaptive control using multiple models, switching and tuning; Inst Engineering Technology-iet; Iet Control Theory And Applications; 5; 18; 12-2011; 2168-2178
1751-8644
1751-8652
url http://hdl.handle.net/11336/14238
identifier_str_mv Giovanini, Leonardo Luis; Robust adaptive control using multiple models, switching and tuning; Inst Engineering Technology-iet; Iet Control Theory And Applications; 5; 18; 12-2011; 2168-2178
1751-8644
1751-8652
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi//10.1049/iet-cta.2010.0550
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 Inst Engineering Technology-iet
publisher.none.fl_str_mv Inst Engineering Technology-iet
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_ 1846782629705154560
score 12.982451

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