Robust coalitional model predictive control with predicted topology transitions

This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce t...

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Detalles Bibliográficos
Autores: Masero Rubio, Eva, Maestre Torreblanca, José María, Ferramosca, Antonio, Francisco, Mario, Camacho, Eduardo F.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/126921
Acceso en línea:https://hdl.handle.net/11441/126921
https://doi.org/10.1109/TCNS.2021.3088806
Access Level:acceso abierto
Palabra clave:Model predictive control
Control by clustering
Distributed control
Coalitional control
Networked control
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spelling Robust coalitional model predictive control with predicted topology transitionsMasero Rubio, EvaMaestre Torreblanca, José MaríaFerramosca, AntonioFrancisco, MarioCamacho, Eduardo F.Model predictive controlControl by clusteringDistributed controlCoalitional controlNetworked controlThis paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict topology transitions to adapt their trajectories while optimizing their goals. Moreover, conditions to guarantee recursive feasibility and robust stability of the system are provided. Finally, the proposed control method is tested via a simulated eight-coupled tanks plant.Ministerio de Ciencia e Innovación FPU18{04476Ministerio de Economía DPI2017-86918-RMinisterio de Economía DPI2015-67341-C02-01Unión Europea No. 789051IEEE (Institute of Electrical and Electronics Engineers)Ingeniería de Sistemas y AutomáticaEuropean Union (UE). H20202021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/126921https://doi.org/10.1109/TCNS.2021.3088806reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Control of Network SystemsFPU18{04476DPI2017-86918-RDPI2015-67341-C02-01No. 789051https://ieeexplore.ieee.org/document/9454295/keywords#keywordsinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1269212026-06-17T12:51:07Z
dc.title.none.fl_str_mv Robust coalitional model predictive control with predicted topology transitions
title Robust coalitional model predictive control with predicted topology transitions
spellingShingle Robust coalitional model predictive control with predicted topology transitions
Masero Rubio, Eva
Model predictive control
Control by clustering
Distributed control
Coalitional control
Networked control
title_short Robust coalitional model predictive control with predicted topology transitions
title_full Robust coalitional model predictive control with predicted topology transitions
title_fullStr Robust coalitional model predictive control with predicted topology transitions
title_full_unstemmed Robust coalitional model predictive control with predicted topology transitions
title_sort Robust coalitional model predictive control with predicted topology transitions
dc.creator.none.fl_str_mv Masero Rubio, Eva
Maestre Torreblanca, José María
Ferramosca, Antonio
Francisco, Mario
Camacho, Eduardo F.
author Masero Rubio, Eva
author_facet Masero Rubio, Eva
Maestre Torreblanca, José María
Ferramosca, Antonio
Francisco, Mario
Camacho, Eduardo F.
author_role author
author2 Maestre Torreblanca, José María
Ferramosca, Antonio
Francisco, Mario
Camacho, Eduardo F.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
European Union (UE). H2020
dc.subject.none.fl_str_mv Model predictive control
Control by clustering
Distributed control
Coalitional control
Networked control
topic Model predictive control
Control by clustering
Distributed control
Coalitional control
Networked control
description This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict topology transitions to adapt their trajectories while optimizing their goals. Moreover, conditions to guarantee recursive feasibility and robust stability of the system are provided. Finally, the proposed control method is tested via a simulated eight-coupled tanks plant.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/126921
https://doi.org/10.1109/TCNS.2021.3088806
url https://hdl.handle.net/11441/126921
https://doi.org/10.1109/TCNS.2021.3088806
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Control of Network Systems
FPU18{04476
DPI2017-86918-R
DPI2015-67341-C02-01
No. 789051
https://ieeexplore.ieee.org/document/9454295/keywords#keywords
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE (Institute of Electrical and Electronics Engineers)
publisher.none.fl_str_mv IEEE (Institute of Electrical and Electronics Engineers)
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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