Adaptive Predictive Control for Industrial Processes

In this work a predictive controller formulation is developed within a linear parameter-varying formalism, which serves as a non-linear process model. The proposed strategy is an adaptive Model-based Predictive Controller (MPC), designed with terminal set constraints and considering the scheduling p...

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Autores principales: Bernardi, Emanuel, Pipino, Hugo, Cappelletti, Carlos A., Adam, Eduardo J.
Formato: Documento de conferencia publisherVersion
Lenguaje:Inglés
Inglés
Publicado: IEEE 2024
Materias:
linear parameter-varying
Solar thermal collector
Model-based predictive control
Non-linear system
Acceso en línea:http://hdl.handle.net/20.500.12272/11235
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-11235
record_format dspace
spelling I68-R174-20.500.12272-112352024-08-05T18:38:16Z Adaptive Predictive Control for Industrial Processes Bernardi, Emanuel Pipino, Hugo Cappelletti, Carlos A. Adam, Eduardo J. linear parameter-varying Solar thermal collector Model-based predictive control Non-linear system In this work a predictive controller formulation is developed within a linear parameter-varying formalism, which serves as a non-linear process model. The proposed strategy is an adaptive Model-based Predictive Controller (MPC), designed with terminal set constraints and considering the scheduling polytope of the model. At each sample time, two Quadratic Programming (QP) problems are solved: the first QP considers a backward horizon to find a virtual model-process tuning variable that defines the best LTI prediction model, considering the vertices of the polytopic system; then, the second QP uses this LTI model to optimise performances along a forward horizon. This paper ends with a realistic solar thermal collector process simulation, comparing the proposed MPC to other techniques from the literature. Discussions regarding the results, the design procedure and the computational effort are presented. Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina. Fil: Pipino, Hugo A. 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. 2024-08-05T18:38:16Z 2024-08-05T18:38:16Z 2021-11-05 info:eu-repo/semantics/conferenceObject publisherVersion 2021 XIX Workshop on Information Processing and Control (RPIC) 978-1-6654-1436-4 http://hdl.handle.net/20.500.12272/11235 10.1109/RPIC53795.2021.9648446 eng eng embargoedAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional . pdf Nacional IEEE
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
Inglés
topic linear parameter-varying
Solar thermal collector
Model-based predictive control
Non-linear system
spellingShingle linear parameter-varying
Solar thermal collector
Model-based predictive control
Non-linear system
Bernardi, Emanuel
Pipino, Hugo
Cappelletti, Carlos A.
Adam, Eduardo J.
Adaptive Predictive Control for Industrial Processes
topic_facet linear parameter-varying
Solar thermal collector
Model-based predictive control
Non-linear system
description In this work a predictive controller formulation is developed within a linear parameter-varying formalism, which serves as a non-linear process model. The proposed strategy is an adaptive Model-based Predictive Controller (MPC), designed with terminal set constraints and considering the scheduling polytope of the model. At each sample time, two Quadratic Programming (QP) problems are solved: the first QP considers a backward horizon to find a virtual model-process tuning variable that defines the best LTI prediction model, considering the vertices of the polytopic system; then, the second QP uses this LTI model to optimise performances along a forward horizon. This paper ends with a realistic solar thermal collector process simulation, comparing the proposed MPC to other techniques from the literature. Discussions regarding the results, the design procedure and the computational effort are presented.
format Documento de conferencia
publisherVersion
author Bernardi, Emanuel
Pipino, Hugo
Cappelletti, Carlos A.
Adam, Eduardo J.
author_facet Bernardi, Emanuel
Pipino, Hugo
Cappelletti, Carlos A.
Adam, Eduardo J.
author_sort Bernardi, Emanuel
title Adaptive Predictive Control for Industrial Processes
title_short Adaptive Predictive Control for Industrial Processes
title_full Adaptive Predictive Control for Industrial Processes
title_fullStr Adaptive Predictive Control for Industrial Processes
title_full_unstemmed Adaptive Predictive Control for Industrial Processes
title_sort adaptive predictive control for industrial processes
publisher IEEE
publishDate 2024
url http://hdl.handle.net/20.500.12272/11235
work_keys_str_mv AT bernardiemanuel adaptivepredictivecontrolforindustrialprocesses
AT pipinohugo adaptivepredictivecontrolforindustrialprocesses
AT cappelletticarlosa adaptivepredictivecontrolforindustrialprocesses
AT adameduardoj adaptivepredictivecontrolforindustrialprocesses
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