Mukhlidi Muskhir, Afdal Luthfi, Muldi Yuhendri, Aswardi Aswardi, Aprilla Fortuna
Direct current (DC) motors are frequently utilized in various applications, and the motor’s pace is affected by applied loads as it fluctuates. A power converter must be employed to control the velocity of the motor by varying the armature voltage. One of the options for the power converter is the one-quadrant DC chopper. In this case, the investigation will turn the one-quadrant chopper into a system by merging velocity and current control into the DC motor. The speed is regulated by controlling the armature voltage. This may be accomplished using a controlled rectifier. The contribution of the research is to test the effectiveness of Artificial Neural Network Control (ANN) and Proportional-Integral (PI) controllers to control the speed of a DC motor using a one-quadrant DC chopper. Therefore, due to technological advancements, the authors will utilize the training data of the artificial neural network of Proportional-Integral controllers in MATLAB’s Simulink. Test results demonstrate the artificial neural network (ANN’s) superior ability to regulate system response, showing enhancements in delay time, rise time, overshoot, and steady-state error compared to the PI controller. These findings underscore the potential of ANN as a more sophisticated choice for DC motor control, although further research is required to fine-tune its performance through rigorous training. © 2024. Mukhlidi Muskhir, Afdal Luthfi, Muldi Yuhendri, Aswardi Aswardi & Aprilla Fortuna; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. The article is published with Open Access at https://www.temjournal.com/
Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia
