Predictive Control Methods for MultiModel Systems

This paper explores the design of three different approaches of robust predictive control formulations for the case of multi-model system representations. The first one is an optimum multi-objective regulator with variable gain matrix that considers a continuous time multi-model system representatio...

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Autores principales: Pipino, Hugo, Bernardi, Emanuel, Cappelletti, Carlos A., Adam, Eduardo J.
Formato: Artículo publisherVersion
Lenguaje:Inglés
Inglés
Publicado: 2024
Materias:
Predictive control
Multi-model
CSTR
Acceso en línea:http://hdl.handle.net/20.500.12272/11239
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-11239
record_format dspace
spelling I68-R174-20.500.12272-112392024-08-26T19:27:57Z Predictive Control Methods for MultiModel Systems Pipino, Hugo Bernardi, Emanuel Cappelletti, Carlos A. Adam, Eduardo J. Predictive control Multi-model CSTR This paper explores the design of three different approaches of robust predictive control formulations for the case of multi-model system representations. The first one is an optimum multi-objective regulator with variable gain matrix that considers a continuous time multi-model system representation and an infinite horizon; the second one is a sub-optimal linear parameter varying model predictive controller based on a discrete time multi-model system representation with finite horizon and a sequence of contractive terminal set constraint; and, at last, an adaptive model predictive controller that considers a discrete time multi-model system representation, with finite horizon and a terminal invariant set, in common to all models within the system’s polytope. Finally, these proposed methods are applied to a continuously-stirred tank reactor (CSTR) system, whose dynamic characteristics are well known and strongly non-linear. Through the simulation results, discussions are established on the design procedure, the online computational effort, the performance indexes and the application difficulties. Fil: Pipino, Hugo A. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina. Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina. Fil: Cappelletti, Carlos A. Universidad Tecnológica Nacional. Facultad Regional Paraná; Argentina. Fil: Adam, Eduardo J. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. Peer Reviewed 2024-08-05T18:44:54Z 2024-08-05T18:44:54Z 2020-12-04 info:eu-repo/semantics/article publisherVersion 2020 IEEE Congreso Bienal de Argentina (ARGENCON) 978-1-7281-5957-7 http://hdl.handle.net/20.500.12272/11239 10.1109/ARGENCON49523.2020.9505546 eng eng embargoedAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional . pdf Nacional
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
Inglés
topic Predictive control
Multi-model
CSTR
spellingShingle Predictive control
Multi-model
CSTR
Pipino, Hugo
Bernardi, Emanuel
Cappelletti, Carlos A.
Adam, Eduardo J.
Predictive Control Methods for MultiModel Systems
topic_facet Predictive control
Multi-model
CSTR
description This paper explores the design of three different approaches of robust predictive control formulations for the case of multi-model system representations. The first one is an optimum multi-objective regulator with variable gain matrix that considers a continuous time multi-model system representation and an infinite horizon; the second one is a sub-optimal linear parameter varying model predictive controller based on a discrete time multi-model system representation with finite horizon and a sequence of contractive terminal set constraint; and, at last, an adaptive model predictive controller that considers a discrete time multi-model system representation, with finite horizon and a terminal invariant set, in common to all models within the system’s polytope. Finally, these proposed methods are applied to a continuously-stirred tank reactor (CSTR) system, whose dynamic characteristics are well known and strongly non-linear. Through the simulation results, discussions are established on the design procedure, the online computational effort, the performance indexes and the application difficulties.
format Artículo
publisherVersion
author Pipino, Hugo
Bernardi, Emanuel
Cappelletti, Carlos A.
Adam, Eduardo J.
author_facet Pipino, Hugo
Bernardi, Emanuel
Cappelletti, Carlos A.
Adam, Eduardo J.
author_sort Pipino, Hugo
title Predictive Control Methods for MultiModel Systems
title_short Predictive Control Methods for MultiModel Systems
title_full Predictive Control Methods for MultiModel Systems
title_fullStr Predictive Control Methods for MultiModel Systems
title_full_unstemmed Predictive Control Methods for MultiModel Systems
title_sort predictive control methods for multimodel systems
publishDate 2024
url http://hdl.handle.net/20.500.12272/11239
work_keys_str_mv AT pipinohugo predictivecontrolmethodsformultimodelsystems
AT bernardiemanuel predictivecontrolmethodsformultimodelsystems
AT cappelletticarlosa predictivecontrolmethodsformultimodelsystems
AT adameduardoj predictivecontrolmethodsformultimodelsystems
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