Reinforcement Learning-Based Control Strategy for BLDC Motors

Abstract

Machine learning has undergone significant advances in recent years, especially in the field of control systems, enabling the development of fully autonomous solutions.This article presents a simulation-based study for the control of a Brushless DC (BLDC) motor using reinforcement learning (RL).A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is implemented as the control strategy.The performance of the proposed controller is evaluated through two test scenarios: one focused on reference tracking accuracy, and the other on robustness under variable torque conditions.The simulation results show a stable and accurate behavior, with response times ranging from 1.2 to 1.9 seconds for reference tracking and from 1.6 to 1.8 seconds in the presence of load disturbances.These performances highlight the ability of reinforcement learning to significantly enhance the precision control of BLDC motors, which remains a challenge in many applications, particularly in complex, dynamic, and nonlinear environments.