Reinforcement learning algorithm for improving speed response of a five-phase permanent magnet synchronous motor based model predictive control.

Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region.