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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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Detalles Bibliográficos
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
Descripción
Sumario:[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.