Reinforcement learning based control approach for PMSM drives — Theory, concept, design and realizations
Nándor Szécsényi, Péter Stumpf · e-Prime - Advances in Electrical Engineering Electronics and Energy · 2025
With the recent advancements made in Artificial Intelligence, it is possible that Reinforcement Learning based data driven methods can become a next generation technology to control electrical drives instead of the classical model-based techniques. However, providing the correct setup and hyperparameters for training the agent is challenging and usually not evident. The manuscript aims to present the main steps of the design workflow to apply Reinforcement Learning for controlling the current of a PMSM drive. Along these steps, every major design consideration is summarized in addition to providing the necessary theoretical background. Furthermore, a thorough examination of the hyperparameter search stage is provided with several experiments in Python on the most influential parameters of the environment. The most optimal setup based on the compared results is selected and evaluated using a testing environment in Matlab that resembles a real-life application scenario. The paper summarize the findings in the form of practical guidelines that are crucial for achieving a high level of performance, and they also minimize the time needed for the implementation and training of the RL agent.