Fault-tolerant Model-based Predictive Control Applied to Industrial Processes

Autores
Bernardi, Emanuel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
tesis doctoral
Estado
versión publicada
Colaborador/a o director/a de tesis
Adam, Eduardo J.
Descripción
Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.
Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina.
Fuente
Doctorado en Tecnología Química (2021)
Materia
Model-based Predictive Control
Fault-tolerance
Fault detection
Fault diagnosis
Non-linear process
Industrial process
Control predictivo basado en modelos
Tolerancia a fallas
Detección de fallas
Diagnóstico de fallas
Proceso no lineal
Industria de procesos
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-10-14T18:50:55Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/11621
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Fault-tolerant Model-based Predictive Control Applied to Industrial ProcessesBernardi, EmanuelModel-based Predictive ControlFault-toleranceFault detectionFault diagnosisNon-linear processIndustrial processControl predictivo basado en modelosTolerancia a fallasDetección de fallasDiagnóstico de fallasProceso no linealIndustria de procesosModern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina.UNLAdam, Eduardo J.2024-10-14T18:50:55Z2024-10-14T18:50:55Z2021-10-21info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_db06info:ar-repo/semantics/tesisDoctoralpdfapplication/pdfhttp://hdl.handle.net/20.500.12272/11621Doctorado en Tecnología Química (2021)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica NacionalengengNacionalinfo:eu-repo/semantics/openAccess2024-10-14T18:50:55Zhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacional.2026-09-24T12:45:59Zoai:ria.utn.edu.ar:20.500.12272/11621instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:45:59.81Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
title Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
spellingShingle Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
Bernardi, Emanuel
Model-based Predictive Control
Fault-tolerance
Fault detection
Fault diagnosis
Non-linear process
Industrial process
Control predictivo basado en modelos
Tolerancia a fallas
Detección de fallas
Diagnóstico de fallas
Proceso no lineal
Industria de procesos
title_short Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
title_full Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
title_fullStr Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
title_full_unstemmed Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
title_sort Fault-tolerant Model-based Predictive Control Applied to Industrial Processes
dc.creator.none.fl_str_mv Bernardi, Emanuel
author Bernardi, Emanuel
author_facet Bernardi, Emanuel
author_role author
dc.contributor.none.fl_str_mv Adam, Eduardo J.
dc.subject.none.fl_str_mv Model-based Predictive Control
Fault-tolerance
Fault detection
Fault diagnosis
Non-linear process
Industrial process
Control predictivo basado en modelos
Tolerancia a fallas
Detección de fallas
Diagnóstico de fallas
Proceso no lineal
Industria de procesos
topic Model-based Predictive Control
Fault-tolerance
Fault detection
Fault diagnosis
Non-linear process
Industrial process
Control predictivo basado en modelos
Tolerancia a fallas
Detección de fallas
Diagnóstico de fallas
Proceso no lineal
Industria de procesos
dc.description.none.fl_txt_mv Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.
Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina.
description Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.
publishDate 2021
dc.date.none.fl_str_mv 2021-10-21
2024-10-14T18:50:55Z
2024-10-14T18:50:55Z
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_db06
info:ar-repo/semantics/tesisDoctoral
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/11621
url http://hdl.handle.net/20.500.12272/11621
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-10-14T18:50:55Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
.
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-10-14T18:50:55Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
.
dc.format.none.fl_str_mv pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv UNL
publisher.none.fl_str_mv UNL
dc.source.none.fl_str_mv Doctorado en Tecnología Química (2021)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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