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Hybrid Approaches-Based Sliding-Mode Control for pH Process Control

[Image: see text] This paper presents two hybrid control topologies; the topologies are designed by combining artificial intelligence approaches and sliding-mode control methodology. The first topology mixes the learning algorithm for multivariable data analysis (LAMDA) approach with sliding-mode co...

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Autores principales: Morales, Luis, Estrada, Juan Sebastian, Herrera, Marco, Rosales, Andres, Leica, Paulo, Gamboa, Silvana, Camacho, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753201/
https://www.ncbi.nlm.nih.gov/pubmed/36530232
http://dx.doi.org/10.1021/acsomega.2c05756
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author Morales, Luis
Estrada, Juan Sebastian
Herrera, Marco
Rosales, Andres
Leica, Paulo
Gamboa, Silvana
Camacho, Oscar
author_facet Morales, Luis
Estrada, Juan Sebastian
Herrera, Marco
Rosales, Andres
Leica, Paulo
Gamboa, Silvana
Camacho, Oscar
author_sort Morales, Luis
collection PubMed
description [Image: see text] This paper presents two hybrid control topologies; the topologies are designed by combining artificial intelligence approaches and sliding-mode control methodology. The first topology mixes the learning algorithm for multivariable data analysis (LAMDA) approach with sliding-mode control. The second offers a Takagi–Sugeno multimodel approach, internal model, and sliding-mode control. The process under study is a nonlinear pH neutralization process with high nonlinearities and time-varying parameters. The pH process is simulated for multiple reference changes, disturbance rejection, and noise in the transmitter. Performance indices are used to compare the proposed approaches quantitatively. The hybrid control topologies enhance the performance and robustness of the pH process under study.
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spelling pubmed-97532012022-12-16 Hybrid Approaches-Based Sliding-Mode Control for pH Process Control Morales, Luis Estrada, Juan Sebastian Herrera, Marco Rosales, Andres Leica, Paulo Gamboa, Silvana Camacho, Oscar ACS Omega [Image: see text] This paper presents two hybrid control topologies; the topologies are designed by combining artificial intelligence approaches and sliding-mode control methodology. The first topology mixes the learning algorithm for multivariable data analysis (LAMDA) approach with sliding-mode control. The second offers a Takagi–Sugeno multimodel approach, internal model, and sliding-mode control. The process under study is a nonlinear pH neutralization process with high nonlinearities and time-varying parameters. The pH process is simulated for multiple reference changes, disturbance rejection, and noise in the transmitter. Performance indices are used to compare the proposed approaches quantitatively. The hybrid control topologies enhance the performance and robustness of the pH process under study. American Chemical Society 2022-12-01 /pmc/articles/PMC9753201/ /pubmed/36530232 http://dx.doi.org/10.1021/acsomega.2c05756 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Morales, Luis
Estrada, Juan Sebastian
Herrera, Marco
Rosales, Andres
Leica, Paulo
Gamboa, Silvana
Camacho, Oscar
Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title_full Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title_fullStr Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title_full_unstemmed Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title_short Hybrid Approaches-Based Sliding-Mode Control for pH Process Control
title_sort hybrid approaches-based sliding-mode control for ph process control
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753201/
https://www.ncbi.nlm.nih.gov/pubmed/36530232
http://dx.doi.org/10.1021/acsomega.2c05756
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