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Vol.13, No.4, November 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Performance Analysis of DC Motors With Integrated Proportional-Integral and Artificial Neural Network Control
Mukhlidi Muskhir, Afdal Luthfi, Muldi Yuhendri, Aswardi Aswardi, Aprilla Fortuna
© 2024 Mukhlidi Muskhir, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 4, Pages 2684-2693, ISSN 2217-8309, DOI: 10.18421/TEM134-06, November 2024.
Received: 26 April 2024 Revised: 30 August 2024.
Abstract:
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 finetune its performance through rigorous training.
Keywords –Artificial neural networks, motor DC, proportional-integral, one quadrant DC chopper. |
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