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...
| Autores: | , |
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| 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 |
| Sumario: | 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. |
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