Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used...

Descripción completa

Detalles Bibliográficos
Autores: Reynoso Meza, Gilberto, Freire, Roberto Z., Sanchís Saez, Javier|||0000-0001-9697-2696, Blasco, Xavier|||0000-0002-9737-2833
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/82090
Acceso en línea:https://riunet.upv.es/handle/10251/82090
Access Level:acceso abierto
Palabra clave:Multi-objective optimisation
Controller tuning
PI tuning
Evolutionary multi-objective optimisation
Preference handling
Many-objective optimisation
INGENIERIA DE SISTEMAS Y AUTOMATICA
id ES_75d6873207bb2e95d9c40aa34ddb2e69
oai_identifier_str oai:riunet.upv.es:10251/82090
network_acronym_str ES
network_name_str España
repository_id_str
spelling Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuningReynoso Meza, GilbertoFreire, Roberto Z.Sanchís Saez, Javier|||0000-0001-9697-2696Blasco, Xavier|||0000-0002-9737-2833Multi-objective optimisationController tuningPI tuningEvolutionary multi-objective optimisationPreference handlingMany-objective optimisationINGENIERIA DE SISTEMAS Y AUTOMATICAMulti-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).ElsevierDepartamento de Ingeniería de Sistemas y AutomáticaInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialConselho Nacional de Desenvolvimento Científico e Tecnológico, BrasilGeneralitat ValencianaRepositorio Institucional de la Universitat Politècnica de València Riunet20162016-06-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/82090reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengConselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 BJT-304804%2F2014-2Generalitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2012%2F028 EVO-CONTROL: CONTROL Y OPTIMIZACION DE PROCESOS INDUSTRIALES BASADO EN ALGORITMOS EVOLUTIVOSopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/820902026-06-13T07:49:27Z
dc.title.none.fl_str_mv Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
title Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
spellingShingle Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
Reynoso Meza, Gilberto
Multi-objective optimisation
Controller tuning
PI tuning
Evolutionary multi-objective optimisation
Preference handling
Many-objective optimisation
INGENIERIA DE SISTEMAS Y AUTOMATICA
title_short Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
title_full Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
title_fullStr Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
title_full_unstemmed Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
title_sort Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning
dc.creator.none.fl_str_mv Reynoso Meza, Gilberto
Freire, Roberto Z.
Sanchís Saez, Javier|||0000-0001-9697-2696
Blasco, Xavier|||0000-0002-9737-2833
author Reynoso Meza, Gilberto
author_facet Reynoso Meza, Gilberto
Freire, Roberto Z.
Sanchís Saez, Javier|||0000-0001-9697-2696
Blasco, Xavier|||0000-0002-9737-2833
author_role author
author2 Freire, Roberto Z.
Sanchís Saez, Javier|||0000-0001-9697-2696
Blasco, Xavier|||0000-0002-9737-2833
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil
Generalitat Valenciana
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Multi-objective optimisation
Controller tuning
PI tuning
Evolutionary multi-objective optimisation
Preference handling
Many-objective optimisation
INGENIERIA DE SISTEMAS Y AUTOMATICA
topic Multi-objective optimisation
Controller tuning
PI tuning
Evolutionary multi-objective optimisation
Preference handling
Many-objective optimisation
INGENIERIA DE SISTEMAS Y AUTOMATICA
description Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/82090
url https://riunet.upv.es/handle/10251/82090
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 BJT-304804%2F2014-2
Generalitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2012%2F028 EVO-CONTROL: CONTROL Y OPTIMIZACION DE PROCESOS INDUSTRIALES BASADO EN ALGORITMOS EVOLUTIVOS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411009041530880
score 15.300724

Similares en LA Referencia