<![CDATA[As electric vehicles (EVs) demand higher performance and efficiency, precise torque control in interior permanent magnet synchronous motors (IPMSMs) becomes increasingly vital. This paper introduces a reinforcement learning (RL)-based method to optimize torque control in IPMSMs. The RL agent is trained to regulate d-axis and q-axis currents, producing stator voltages to follow the desired motor speed. The control system includes an observation vector, voltage-based actions, and a specially designed reward function. Due to the nonlinear dynamics of the motor, training the agent requires significant computational effort. MATLAB/Simulink simulations are performed to compare the RL controller with a traditional PI controller. Results indicate that the RL controller delivers quicker and more accurate performance, although additional training is necessary to minimize overshoot.]]>
Copyrights © 2025
