A Deep Reinforcement Learning Paradigm for DC Motor Speed Control

This research explores the application of deep reinforcement learning (DRL) to enhance the speed control of DC motor, which is widely utilized in robotics, automation industry, autonomous vehicles, and drones. The efficiency offered by DC motors make them integral to energy-saving propulsion systems, supporting the development of sustainable transportation technologies. However, despite their advantages, DC motors face significant challenges in complex systems, such as precise speed regulation and consistent acceleration or deceleration. Traditional control schemes, such as Proportional-Integral (PI) controllers, often fall short in addressing these challenges effectively. To overcome these limitations, this study leverages DRL, specifically the Deterministic Policy Gradient (DDPG) algorithm, to learn an optimal control policy in a simulation environment. A comprehensive simulation model of a DC motor is developed in MATLAB Simulink to mimic the system’s dynamic behavior. The proposed approach is then compared to the traditional PI controller to evaluate its effectiveness. The results demonstrate the superior performance of the DDPG-based controller in terms of speed regulation accuracy, thus highlighting the potential of advanced DRL techniques in addressing the challenges associated with DC motor control.