Discrete-time MPC for switched systems with applications to biomedical problems

Autores
Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; Hernandez Vargas, Esteban Abelardo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Switched systems in which the manipulated control action is the time-dependingswitching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Two biomedical applications are simulated to test the controller: (i) the drug schedule to attenuate the effect of viralmutation and drugs resistance on the viral load, and (ii) the drug schedule for Triple Negative breast cancer treatment. The numerical results suggest that the proposed strategy outperform the schedule for available treatments.
Fil: Anderson, Alejandro Luis. 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
Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional; Argentina
Fil: Hernandez Vargas, Esteban Abelardo. Frankfurt Institute For Advanced Studies-fias; Alemania
Materia
Model Predictive Control
Switched Systems
Scheduling Treatment
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:11336/110283
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Discrete-time MPC for switched systems with applications to biomedical problemsAnderson, Alejandro LuisGonzález, Alejandro HernánFerramosca, AntonioHernandez Vargas, Esteban AbelardoModel Predictive ControlSwitched SystemsScheduling Treatmenthttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Switched systems in which the manipulated control action is the time-dependingswitching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Two biomedical applications are simulated to test the controller: (i) the drug schedule to attenuate the effect of viralmutation and drugs resistance on the viral load, and (ii) the drug schedule for Triple Negative breast cancer treatment. The numerical results suggest that the proposed strategy outperform the schedule for available treatments.Fil: Anderson, Alejandro Luis. 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; ArgentinaFil: Ferramosca, Antonio. Universidad Tecnológica Nacional; ArgentinaFil: Hernandez Vargas, Esteban Abelardo. Frankfurt Institute For Advanced Studies-fias; AlemaniaCornell University2020-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/110283Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; Hernandez Vargas, Esteban Abelardo; Discrete-time MPC for switched systems with applications to biomedical problems; Cornell University; arXiv; 6-2020; 1-222331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/pdf/2006.12936.pdfinfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2006.12936info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:01:28Zoai:ri.conicet.gov.ar:11336/110283instacron: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 10:01:29.088CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Discrete-time MPC for switched systems with applications to biomedical problems
title Discrete-time MPC for switched systems with applications to biomedical problems
spellingShingle Discrete-time MPC for switched systems with applications to biomedical problems
Anderson, Alejandro Luis
Model Predictive Control
Switched Systems
Scheduling Treatment
title_short Discrete-time MPC for switched systems with applications to biomedical problems
title_full Discrete-time MPC for switched systems with applications to biomedical problems
title_fullStr Discrete-time MPC for switched systems with applications to biomedical problems
title_full_unstemmed Discrete-time MPC for switched systems with applications to biomedical problems
title_sort Discrete-time MPC for switched systems with applications to biomedical problems
dc.creator.none.fl_str_mv Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
Hernandez Vargas, Esteban Abelardo
author Anderson, Alejandro Luis
author_facet Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
Hernandez Vargas, Esteban Abelardo
author_role author
author2 González, Alejandro Hernán
Ferramosca, Antonio
Hernandez Vargas, Esteban Abelardo
author2_role author
author
author
dc.subject.none.fl_str_mv Model Predictive Control
Switched Systems
Scheduling Treatment
topic Model Predictive Control
Switched Systems
Scheduling Treatment
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Switched systems in which the manipulated control action is the time-dependingswitching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Two biomedical applications are simulated to test the controller: (i) the drug schedule to attenuate the effect of viralmutation and drugs resistance on the viral load, and (ii) the drug schedule for Triple Negative breast cancer treatment. The numerical results suggest that the proposed strategy outperform the schedule for available treatments.
Fil: Anderson, Alejandro Luis. 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
Fil: Ferramosca, Antonio. Universidad Tecnológica Nacional; Argentina
Fil: Hernandez Vargas, Esteban Abelardo. Frankfurt Institute For Advanced Studies-fias; Alemania
description Switched systems in which the manipulated control action is the time-dependingswitching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Two biomedical applications are simulated to test the controller: (i) the drug schedule to attenuate the effect of viralmutation and drugs resistance on the viral load, and (ii) the drug schedule for Triple Negative breast cancer treatment. The numerical results suggest that the proposed strategy outperform the schedule for available treatments.
publishDate 2020
dc.date.none.fl_str_mv 2020-06
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/11336/110283
Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; Hernandez Vargas, Esteban Abelardo; Discrete-time MPC for switched systems with applications to biomedical problems; Cornell University; arXiv; 6-2020; 1-22
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/110283
identifier_str_mv Anderson, Alejandro Luis; González, Alejandro Hernán; Ferramosca, Antonio; Hernandez Vargas, Esteban Abelardo; Discrete-time MPC for switched systems with applications to biomedical problems; Cornell University; arXiv; 6-2020; 1-22
2331-8422
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://arxiv.org/pdf/2006.12936.pdf
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2006.12936
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
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
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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