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

Autores
Anderson, A; González, Alejandro; Ferramosca, Antonio; Hernandez - Vargas, E
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-depending switching 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 theMPC 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 viral mutation 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 A. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.
Fil: Gonzales, Alejandro. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.
Fil: Ferramosca, Antonio. CONICET - Universidad Tecnológica Nacional (UTN). Facultad Regional Reconquista. Argentina.
Fil: Hernandez - Vargas. Institute of Mathematics, UNAM, Mexico. Research Fellow, Frankfurt Institute for Advanced Studies. Germany.
Peer Reviewed
Materia
Model Predictive Control, Switched Systems, Stability, Biomedical Treatment, Resistance.
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2021-03-11T21:23:08Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/4872
Acceder
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Discrete-time MPC for switched systems with applications to biomedical problemsAnderson, AGonzález, AlejandroFerramosca, AntonioHernandez - Vargas, EModel Predictive Control, Switched Systems, Stability, Biomedical Treatment, Resistance.Switched systems in which the manipulated control action is the time-depending switching 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 theMPC 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 viral mutation 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 A. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.Fil: Gonzales, Alejandro. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.Fil: Ferramosca, Antonio. CONICET - Universidad Tecnológica Nacional (UTN). Facultad Regional Reconquista. Argentina.Fil: Hernandez - Vargas. Institute of Mathematics, UNAM, Mexico. Research Fellow, Frankfurt Institute for Advanced Studies. Germany.Peer Reviewed2021-03-11T21:23:08Z2021-03-11T21:23:08Z2020-06-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/110283http://hdl.handle.net/20.500.12272/4872enginfo:eu-repo/semantics/openAccess2021-03-11T21:23:08Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalN/Areponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-10-01T12:00:25Zoai:ria.utn.edu.ar:20.500.12272/4872instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 12:00:25.838Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
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, A
Model Predictive Control, Switched Systems, Stability, Biomedical Treatment, Resistance.
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, A
González, Alejandro
Ferramosca, Antonio
Hernandez - Vargas, E
author Anderson, A
author_facet Anderson, A
González, Alejandro
Ferramosca, Antonio
Hernandez - Vargas, E
author_role author
author2 González, Alejandro
Ferramosca, Antonio
Hernandez - Vargas, E
author2_role author
author
author
dc.subject.none.fl_str_mv Model Predictive Control, Switched Systems, Stability, Biomedical Treatment, Resistance.
topic Model Predictive Control, Switched Systems, Stability, Biomedical Treatment, Resistance.
dc.description.none.fl_txt_mv Switched systems in which the manipulated control action is the time-depending switching 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 theMPC 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 viral mutation 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 A. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.
Fil: Gonzales, Alejandro. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL). Argentina.
Fil: Ferramosca, Antonio. CONICET - Universidad Tecnológica Nacional (UTN). Facultad Regional Reconquista. Argentina.
Fil: Hernandez - Vargas. Institute of Mathematics, UNAM, Mexico. Research Fellow, Frankfurt Institute for Advanced Studies. Germany.
Peer Reviewed
description Switched systems in which the manipulated control action is the time-depending switching 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 theMPC 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 viral mutation 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 2020-06-23
2021-03-11T21:23:08Z
2021-03-11T21:23:08Z
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
http://hdl.handle.net/20.500.12272/4872
url http://hdl.handle.net/11336/110283
http://hdl.handle.net/20.500.12272/4872
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2021-03-11T21:23:08Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
N/A
eu_rights_str_mv openAccess
rights_invalid_str_mv 2021-03-11T21:23:08Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
N/A
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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