Assessing deep reinforcement learning control of permanent magnet- assisted synchronous reluctance machines

This paper presents an application of deep reinforcement learning (DRL) for controlling permanent magnet-assisted synchronous reluctance machines (PMA SynRMs). A model-free DRL agent is trained to control the power converter switching states, aiming to accurately track current references. The DRL-based control scheme is compared against a traditional finite control set model predictive control (FCS-MPC) strategy employing a simplified linear model of the PMA-SynRM. Simulation results demonstrate that the DRL controller achieves superior performance in terms of tracking accuracy and harmonic distortion reduction, effectively handling the machine's inherent nonlinearities. Furthermore, the DRL agent exhibits robustness against measurement errors. The findings highlight the potential of DRL as a viable alternative to conventional model-based control methods for high performance PMA-SynRM drives, offering improved adaptability, robustness, and operational flexibility. Key words. Artificial neural networks, deep reinforcement learning, electrical drives, model predictive control, permanent magnet-assisted synchronous reluctance machines.