DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision - N. N. Shaikh; I.R Kazi Kutubuddin Sayyad Liyakat (International Journal of Electrical Machine Analysis and Design, 2025)
DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a Proximal Policy Optimization (PPO) agent—to govern the speed and torque of a brushed DC motor. The PPO agent interacts directly with a high-fidelity motor simulation environment, learning optimal control policies by maximizing a custom reward function that penalizes overshoot, settling time, and steady-state error. The resulting AI controller demonstrates exceptional adaptation and robustness. Experimental validation shows that the DRL-based system achieves a 60% reduction in peak overshoot and a 45% faster settling time compared to a meticulously fine-tuned cascaded PID controller, particularly during abrupt load application and removal. By bypassing the need for explicit mathematical modeling, this approach provides a scalable, intelligent solution that mitigates common control challenges, paving the way for truly autonomous, highly resilient electromechanical systems.
Keywords: DC Motor; Deep Learning; Deep Reinforced Learning; Proximal Policy Optimization