Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system

The control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be use...

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Detalles Bibliográficos
Autores: Aparna, V., Jamal, D.N.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:México
Institución:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
Repositorio:Journal of Applied Research and Technology
Idioma:inglés
OAI Identifier:oai:ojs2.localhost:article/1697
Acceso en línea:https://jart.icat.unam.mx/index.php/jart/article/view/1697
Access Level:acceso abierto
Palabra clave:pH Control
PID Controller
Genetic Algorithm
Hybrid Bacterial Foraging Technique
Particle Swarm Optimization
Quantitative Feedback Theory
Robust Control
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spelling Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization systemAparna, V.Jamal, D.N.pH ControlPID ControllerGenetic AlgorithmHybrid Bacterial Foraging TechniqueParticle Swarm OptimizationQuantitative Feedback TheoryRobust ControlThe control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be used, and the pH system was thus represented as a linear state-space model around an equilibrium point. This linear model was then used to compute the PID controller gains using robust and optimization techniques like quantitative feedback theory, bacterial foraging technique-based particle swarm optimization algorithm, and genetic algorithm. The corresponding controller gains resulting from the three algorithms were used to control the pH using a reconfigurable I/O device, NI myRIO-1900. Finally, the output time domain specifications and the servo and regulatory responses, resulting from the three algorithms, were compared in simulation and in real-time to deduce the appropriate tuning algorithm for this system.Universidad Nacional Autónoma de México2021-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://jart.icat.unam.mx/index.php/jart/article/view/169710.22201/icat.24486736e.2021.19.3.1697Journal of Applied Research and Technology; Vol. 19 No. 3 (2021); 263-278Journal of Applied Research and Technology; Vol. 19 Núm. 3 (2021); 263-2782448-67361665-642310.22201/icat.24486736e.2021.19.3reponame:Journal of Applied Research and Technologyinstname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICOinstacron:UNAMenghttps://jart.icat.unam.mx/index.php/jart/article/view/1697/821Copyright (c) 2021 Universidad Nacional Autónoma de Méxicoinfo:eu-repo/semantics/openAccessoai:ojs2.localhost:article/16972024-08-16T17:54:20Z
dc.title.none.fl_str_mv Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
title Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
spellingShingle Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
Aparna, V.
pH Control
PID Controller
Genetic Algorithm
Hybrid Bacterial Foraging Technique
Particle Swarm Optimization
Quantitative Feedback Theory
Robust Control
title_short Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
title_full Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
title_fullStr Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
title_full_unstemmed Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
title_sort Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system
dc.creator.none.fl_str_mv Aparna, V.
Jamal, D.N.
author Aparna, V.
author_facet Aparna, V.
Jamal, D.N.
author_role author
author2 Jamal, D.N.
author2_role author
dc.subject.none.fl_str_mv pH Control
PID Controller
Genetic Algorithm
Hybrid Bacterial Foraging Technique
Particle Swarm Optimization
Quantitative Feedback Theory
Robust Control
topic pH Control
PID Controller
Genetic Algorithm
Hybrid Bacterial Foraging Technique
Particle Swarm Optimization
Quantitative Feedback Theory
Robust Control
description The control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be used, and the pH system was thus represented as a linear state-space model around an equilibrium point. This linear model was then used to compute the PID controller gains using robust and optimization techniques like quantitative feedback theory, bacterial foraging technique-based particle swarm optimization algorithm, and genetic algorithm. The corresponding controller gains resulting from the three algorithms were used to control the pH using a reconfigurable I/O device, NI myRIO-1900. Finally, the output time domain specifications and the servo and regulatory responses, resulting from the three algorithms, were compared in simulation and in real-time to deduce the appropriate tuning algorithm for this system.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-30
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/1697
10.22201/icat.24486736e.2021.19.3.1697
url https://jart.icat.unam.mx/index.php/jart/article/view/1697
identifier_str_mv 10.22201/icat.24486736e.2021.19.3.1697
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://jart.icat.unam.mx/index.php/jart/article/view/1697/821
dc.rights.none.fl_str_mv Copyright (c) 2021 Universidad Nacional Autónoma de México
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Universidad Nacional Autónoma de México
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional Autónoma de México
publisher.none.fl_str_mv Universidad Nacional Autónoma de México
dc.source.none.fl_str_mv Journal of Applied Research and Technology; Vol. 19 No. 3 (2021); 263-278
Journal of Applied Research and Technology; Vol. 19 Núm. 3 (2021); 263-278
2448-6736
1665-6423
10.22201/icat.24486736e.2021.19.3
reponame:Journal of Applied Research and Technology
instname:UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron:UNAM
instname_str UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
instacron_str UNAM
institution UNAM
reponame_str Journal of Applied Research and Technology
collection Journal of Applied Research and Technology
repository.name.fl_str_mv
repository.mail.fl_str_mv
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