A Comparative Study of Traditional PID Tuning Techniques and AI-Based Algorithmic Approaches Utilizing the Python Control Library
DOI:
https://doi.org/10.33019/jurnalecotipe.v12i1.4574Keywords:
Artificial Intelligence, Genetic Algorithm, Particle Swarm Optimization, PID Parameter, Ziegler-NicholsAbstract
This study aims to compare PID parameter settings with conventional tuning methods and tune methods using AI (artificial intelligence) algorithms. This study was conducted by means of simulation using a computer program created in Python and utilizing AI libraries to solve the problem of determining PID (proportional-integral-derivative) parameters. Two AI algorithms used in this study, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods, were compared with the conventional Ziegler-Nichols method. The study was conducted by applying the PID parameters obtained to a certain transfer function and then comparing them on several related aspects. The results of the study showed that the solution obtained using the AI method requires a longer execution time, more than 2 seconds for PSO and more than 3 seconds for GA, while ZN requires less than 1 second. However, the AI method can provide better solutions, as can be seen from the magnitude of the ITAE that occurs, where GA and PSO provide ITAE less than 1 while ZN is more than 22.
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