Speed Control of IM Using RL-Based TD3 Agent

Induction machines (IM) have been used daily since the 19
^th
century. The induction machine should be effectively controlled to achieve high performance. Since the late 20
^th
century and 21
^st
century, field-oriented control (FOC), direct torque control (DTC), and model predictive control (MPC) techniques have been used in high-performance control applications. However, these techniques are dependent on the motor parameters and inverter model. These parameters change non-linearly depending on magnetic saturation, temperature, and operating point which negatively affects the performance of the system. In order to eliminate the negative effects of parameter changes, reinforcement learning (RL)based methods have become increasingly popular in the literature in recent years. In this study, for the first time, the speed control of IM is performed using TD3 agent, which is one of the RL-based methods. The dynamic and steady-state performance of the control system designed with the TD3 agent is compared with the traditional FOC technique. Extensive simulation results have shown the robustness of the proposed drive system.