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Revisiting Classical Controllers Design and Tuning with Genetic Programming
Version 1
: Received: 21 November 2023 / Approved: 21 November 2023 / Online: 22 November 2023 (07:49:09 CET)
A peer-reviewed article of this Preprint also exists.
García, C.A.; Velasco, M.; Angulo, C.; Marti, P.; Camacho, A. Revisiting Classical Controller Design and Tuning with Genetic Programming. Sensors 2023, 23, 9731. García, C.A.; Velasco, M.; Angulo, C.; Marti, P.; Camacho, A. Revisiting Classical Controller Design and Tuning with Genetic Programming. Sensors 2023, 23, 9731.
Abstract
This paper introduces the implementation of a genetic programming (GP)-based procedure to the automatic design and tuning of process controllers. The proposed approach makes a significant contribution to the field of artificial intelligence (AI) in control engineering. Unlike other controller design methods, the GP-based program handles the entire design in the time domain, including differential operations like derivatives and integrals, without the need for intermediate inverse Laplace transformation. This approach not only simplifies the design process but also ensures that all generated controllers are implementable in physical systems. Furthermore, GP’s functions set includes various mathematical operations beyond basic arithmetic operators, such as trigonometric, exponential, and logarithmic operators. The performance and validity of the resulting controllers generated by the proposed GP-based approach are evaluated by verifying whether the generator can replicate the structure and performance of those produced by traditional controller design methods and, in some cases, achieve even better results. As a result, the GP-based approach presents a promising solution for automating the controller design process and addressing control problems in various engineering applications.
Keywords
genetic algorithm; genetic programming; control design; control tuning
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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