IMPROVING EFFICIENCY IN DC MOTORS: A COMPARISON BETWEEN PID-FUZZY AND PID-GENETIC ALGORITHM

Paper Details

Authors: Leite E.O.; Pardinho M.M.; Grillo D.R.; Reis J.S.D.M.; Santos G.; Barbosa L.C.F.M.

DOI: 10.24874/PES08.01.002

Journal: Proceedings on Engineering Sciences

Year: 2026

Publisher: Faculty of Engineering, University of Kragujevac

Document Type: Article

Open Access: All Open Access; Gold Open Access; Green Open Access

Cited by: 0

Abstract

With the advent of Industry 4.0 and the continued progression of automation driven by Artificial Intelligence (AI), optimizing industrial processes has become a critical need. The growing preference for brushless DC motors in a variety of industrial sectors can be attributed to their significant advantages, such as high efficiency, low maintenance requirements and speed control capabilities, in contrast to conventional induction motors. Against this backdrop, this research set out to examine the effectiveness of two controller optimization methods incorporated with Proportional Integral Derivative-FUZZY and Proportional Integral Derivative-Genetic Algorithm, for the control of DC electric motors. The results showed that the Proportional Integral Derivative-FUZZY method achieved superior performance compared to the Proportional Integral Derivative-Genetic Algorithm, considering the parameters and criteria defined, and is an effective, versatile and robust method for controlling these motor systems. © 2026 Published by Faculty of Engineeringg.

Keywords

Artificial Intelligence; DC Motors; Industry 4.0; PID Controller; Process Control

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