Model predictive control with PWA models

Descripción del Articulo

This article presents on controlling a fishmeal dryer. A model-based predictive controller was designed to control the amount of moisture present in fishmeal. The Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC) approach has been used. This approach uses a non-linear model in its impleme...

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Detalles Bibliográficos
Autores: Oliden J., Ipanaque W.
Formato: artículo
Fecha de Publicación:2020
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/2471
Enlace del recurso:https://hdl.handle.net/20.500.12390/2471
https://doi.org/10.1109/EIRCON51178.2020.9254054
Nivel de acceso:acceso abierto
Materia:PWA
NEPSAC
non linear control
http://purl.org/pe-repo/ocde/ford#2.02.04
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spelling Publicationrp06268600rp05418600Oliden J.Ipanaque W.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2020https://hdl.handle.net/20.500.12390/2471https://doi.org/10.1109/EIRCON51178.2020.92540542-s2.0-85097827581This article presents on controlling a fishmeal dryer. A model-based predictive controller was designed to control the amount of moisture present in fishmeal. The Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC) approach has been used. This approach uses a non-linear model in its implementation. In this work we study the use of piecewise affine (PWA) models to approximate the non-linear model. The use of PWA models reduces calculation time but worsens the performance of the controller. © 2020 IEEE.Fondo Nacional de Desarrollo Científico y Tecnológico - FondecytengInstitute of Electrical and Electronics Engineers Inc.Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020info:eu-repo/semantics/openAccessPWANEPSAC-1non linear control-1non linear control-1http://purl.org/pe-repo/ocde/ford#2.02.04-1Model predictive control with PWA modelsinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/2471oai:repositorio.concytec.gob.pe:20.500.12390/24712024-05-30 15:24:46.305http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c2804fc4-2f48-4085-bcc5-e7f90b87785e"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Model predictive control with PWA models</Title> <PublishedIn> <Publication> <Title>Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020</Title> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <DOI>https://doi.org/10.1109/EIRCON51178.2020.9254054</DOI> <SCP-Number>2-s2.0-85097827581</SCP-Number> <Authors> <Author> <DisplayName>Oliden J.</DisplayName> <Person id="rp06268" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Ipanaque W.</DisplayName> <Person id="rp05418" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Institute of Electrical and Electronics Engineers Inc.</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>PWA</Keyword> <Keyword>NEPSAC</Keyword> <Keyword>non linear control</Keyword> <Keyword>non linear control</Keyword> <Abstract>This article presents on controlling a fishmeal dryer. A model-based predictive controller was designed to control the amount of moisture present in fishmeal. The Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC) approach has been used. This approach uses a non-linear model in its implementation. In this work we study the use of piecewise affine (PWA) models to approximate the non-linear model. The use of PWA models reduces calculation time but worsens the performance of the controller. © 2020 IEEE.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv Model predictive control with PWA models
title Model predictive control with PWA models
spellingShingle Model predictive control with PWA models
Oliden J.
PWA
NEPSAC
non linear control
non linear control
http://purl.org/pe-repo/ocde/ford#2.02.04
title_short Model predictive control with PWA models
title_full Model predictive control with PWA models
title_fullStr Model predictive control with PWA models
title_full_unstemmed Model predictive control with PWA models
title_sort Model predictive control with PWA models
author Oliden J.
author_facet Oliden J.
Ipanaque W.
author_role author
author2 Ipanaque W.
author2_role author
dc.contributor.author.fl_str_mv Oliden J.
Ipanaque W.
dc.subject.none.fl_str_mv PWA
topic PWA
NEPSAC
non linear control
non linear control
http://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.es_PE.fl_str_mv NEPSAC
non linear control
non linear control
dc.subject.ocde.none.fl_str_mv http://purl.org/pe-repo/ocde/ford#2.02.04
description This article presents on controlling a fishmeal dryer. A model-based predictive controller was designed to control the amount of moisture present in fishmeal. The Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC) approach has been used. This approach uses a non-linear model in its implementation. In this work we study the use of piecewise affine (PWA) models to approximate the non-linear model. The use of PWA models reduces calculation time but worsens the performance of the controller. © 2020 IEEE.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/2471
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/EIRCON51178.2020.9254054
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85097827581
url https://hdl.handle.net/20.500.12390/2471
https://doi.org/10.1109/EIRCON51178.2020.9254054
identifier_str_mv 2-s2.0-85097827581
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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score 13.413286
Model predictive control with PWA models
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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