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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/14238
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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 Técnicas2025-09-29T10:45:56Zoai: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:34982025-09-29 10:45:56.941CONICET 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_ |
1844614499901898752 |
score |
13.070432 |