MPC for linear systems with parametric uncertainty

This paper deals with linear systems with paramet- ric uncertainty using a model-based predictive control (MPC). When the uncertainty of the system is significant, the MPC performance can be deteriorated or even the optimization problem can be unfeasible. In this paper, a MPC for linear systems wit...

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Autores principales: Pipino, Hugo, Adam, Eduardo J.
Formato: Documento de conferencia publisherVersion
Lenguaje:Inglés
Inglés
Publicado: IEEE 2024
Materias:
model predictive control
LPV system
feasibility
stability
reachability
Acceso en línea:http://hdl.handle.net/20.500.12272/11237
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-11237
record_format dspace
spelling I68-R174-20.500.12272-112372024-08-05T18:42:12Z MPC for linear systems with parametric uncertainty Pipino, Hugo Adam, Eduardo J. model predictive control LPV system feasibility stability reachability This paper deals with linear systems with paramet- ric uncertainty using a model-based predictive control (MPC). When the uncertainty of the system is significant, the MPC performance can be deteriorated or even the optimization problem can be unfeasible. In this paper, a MPC for linear systems with parametric uncertainty is presented. This controller considers the weight variable of a linear parameter-varying (LPV) system as a decision variable of the optimization problem and a terminal invariant set for all the systems that are within the uncertainty polytope. Finally, this controller is applied to a mass-spring-damper system to verify its properties. Fil: Pipino, Hugo. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina. Fil: Adam, Eduardo J. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. 2024-08-05T18:42:12Z 2024-08-05T18:42:12Z 2019-09-20 info:eu-repo/semantics/conferenceObject publisherVersion 2019 XVIII Workshop on Information Processing and Control (RPIC) 978-1-7281-2363-9 http://hdl.handle.net/20.500.12272/11237 10.1109/RPIC.2019.8882151 eng eng embargoedAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional . pdf Nacional IEEE 2019 XVIII Workshop on Information Processing and Control (RPIC): 42 - 47 (2019).
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
Inglés
topic model predictive control
LPV system
feasibility
stability
reachability
spellingShingle model predictive control
LPV system
feasibility
stability
reachability
Pipino, Hugo
Adam, Eduardo J.
MPC for linear systems with parametric uncertainty
topic_facet model predictive control
LPV system
feasibility
stability
reachability
description This paper deals with linear systems with paramet- ric uncertainty using a model-based predictive control (MPC). When the uncertainty of the system is significant, the MPC performance can be deteriorated or even the optimization problem can be unfeasible. In this paper, a MPC for linear systems with parametric uncertainty is presented. This controller considers the weight variable of a linear parameter-varying (LPV) system as a decision variable of the optimization problem and a terminal invariant set for all the systems that are within the uncertainty polytope. Finally, this controller is applied to a mass-spring-damper system to verify its properties.
format Documento de conferencia
publisherVersion
author Pipino, Hugo
Adam, Eduardo J.
author_facet Pipino, Hugo
Adam, Eduardo J.
author_sort Pipino, Hugo
title MPC for linear systems with parametric uncertainty
title_short MPC for linear systems with parametric uncertainty
title_full MPC for linear systems with parametric uncertainty
title_fullStr MPC for linear systems with parametric uncertainty
title_full_unstemmed MPC for linear systems with parametric uncertainty
title_sort mpc for linear systems with parametric uncertainty
publisher IEEE
publishDate 2024
url http://hdl.handle.net/20.500.12272/11237
work_keys_str_mv AT pipinohugo mpcforlinearsystemswithparametricuncertainty
AT adameduardoj mpcforlinearsystemswithparametricuncertainty
_version_ 1809230384723918848

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