Stochastic model predictive control for tracking linear systems

This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any chan...

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Autores principales: D' Jorge, Agustina, Santoro, Bruno, Anderson, Alejandro Luis, González, Alejandro Hernán, Ferramosca, Antonio
Formato: Artículo publisherVersion
Lenguaje:Inglés
Publicado: 2021
Materias:
MODEL PREDICTIVE CONTROL , PROBABILISTIC CONSTRAINTS , STOCHASTIC CONTROL , TRACKING
Acceso en línea:http://hdl.handle.net/20.500.12272/4923
https://doi.org/10.1002/oca.2501
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-4923
record_format dspace
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic MODEL PREDICTIVE CONTROL , PROBABILISTIC CONSTRAINTS , STOCHASTIC CONTROL , TRACKING
spellingShingle MODEL PREDICTIVE CONTROL , PROBABILISTIC CONSTRAINTS , STOCHASTIC CONTROL , TRACKING
D' Jorge, Agustina
Santoro, Bruno
Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
Stochastic model predictive control for tracking linear systems
topic_facet MODEL PREDICTIVE CONTROL , PROBABILISTIC CONSTRAINTS , STOCHASTIC CONTROL , TRACKING
description This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction, even larger than the one delivered by Lorenzen et al, thanks to the use of artificial references and relaxed terminal constraints. The asymptotic stability (in probability) of the minimal robust positively invariant set centered on the desired setpoint is guaranteed. Simulations on a DC-DC converter show the benefits and the properties of the proposal.
format Artículo
publisherVersion
author D' Jorge, Agustina
Santoro, Bruno
Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
author_facet D' Jorge, Agustina
Santoro, Bruno
Anderson, Alejandro Luis
González, Alejandro Hernán
Ferramosca, Antonio
author_sort D' Jorge, Agustina
title Stochastic model predictive control for tracking linear systems
title_short Stochastic model predictive control for tracking linear systems
title_full Stochastic model predictive control for tracking linear systems
title_fullStr Stochastic model predictive control for tracking linear systems
title_full_unstemmed Stochastic model predictive control for tracking linear systems
title_sort stochastic model predictive control for tracking linear systems
publishDate 2021
url http://hdl.handle.net/20.500.12272/4923
https://doi.org/10.1002/oca.2501
work_keys_str_mv AT djorgeagustina stochasticmodelpredictivecontrolfortrackinglinearsystems
AT santorobruno stochasticmodelpredictivecontrolfortrackinglinearsystems
AT andersonalejandroluis stochasticmodelpredictivecontrolfortrackinglinearsystems
AT gonzalezalejandrohernan stochasticmodelpredictivecontrolfortrackinglinearsystems
AT ferramoscaantonio stochasticmodelpredictivecontrolfortrackinglinearsystems
bdutipo_str Repositorios
_version_ 1764820552279654400

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