Reinforcement Learning-Based Predictive Control for Power Electronic Converters

Abstract

Finite-set model predictive control (FS-MPC) appears to be a promising and effective control method for power electronic converters. Conventional FS-MPC suffers from the time-consuming process of weighting factor selection, which significantly impacts control performance. Another ongoing challenge of FS-MPC is its dependence on the prediction model for desirable control performance. To overcome the above issues, we propose to apply reinforcement learning (RL) to FS-MPC for power converters. The RL algorithm is first employed for the automatic weighting factor design of the FS-MPC, aiming to minimize the total harmonic distortion (THD) or reduce the average switching frequency. Furthermore, by formulating the incentive for the RL agent with the cost function of the predictive algorithm, the agent learns autonomously to find the optimal switching policy for the power converter by imitating the predictive controller without prior knowledge of the system model. Finally, a deployment framework that allows for experimental validation of the proposed RL-based methods on a practical FS-MPC regulated stand-alone converter configuration is presented. Two exemplary control objectives are demonstrated to show the effectiveness of the proposed RL-aided weighting factor tuning method. Moreover, the results show a good match between the model-free RL-based controller and the FS-MPC performance.