PID Controller Tuning for Speed Control of a Direct Current Motor using Genetic Algorithm
DOI:
https://doi.org/10.47187/perspectivas.vol1iss2.pp31-37.2019Keywords:
Genetic Algorithm, PID Control, Artificial Intelligence, Optimal ControlAbstract
This document presents the development of a genetic algorithm for optimizing the gains of a PID (proportional, integral, derivative) controller applied to the speed control of a direct current motor. The algorithm was developed in Python code. It produces a good performance with few iterations due to the generation of the initial population based on the tuning rules of Ziegler & Nichols. The controller obtained through the application of the genetic algorithm is compared with the conventional tuning methods of Ziegler and Nichols, Cohen-Coon and AMIGO, in terms of establishment time, maximum overshoot and robustness. The obtained results allow to conclude that the maximum overshoot and the establishment time are minimized by using the controller obtained through the genetic algorithm, which in turn has a better robustness compared to the controllers obtained with the other methods.
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Copyright (c) 2019 Ronald Marcelo Barcia Macías, Sofia Elizabeth Berrones Asqui, Julio-Ariel Romero-Pérez, Oscar Miguel-Escrig
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