This paper proposes an innovative control strategy for DC-DC LLC resonant converters, which is based on Reinforcement Learning (RL), specifically utilizing the Tabular Q-Learning algorithm. The presented approach is designed to overcome the limitations of traditional model-based linear controllers and offers two distinct advantages. First, the model-free nature of the algorithm ensures superior robustness: the agent learns the optimal control policy through direct interaction with the converter, implicitly compensating for non-linearities, component tolerances, and parameter drifts caused by aging or thermal stress, without requiring a priori knowledge of the mathematical model. Second, unlike Deep Reinforcement Learning (DRL) techniques, which demand high processing power, the tabular approach guarantees a fast, deterministic execution, which makes the proposed technique highly suitable for implementation on standard microcontrollers in low-cost edge applications. Validation through PLECS simulations demonstrates the controller’s ability to maintain tight voltage regulation even under severe dynamic variations of the input voltage and load.
Corti et al. (Thu,) studied this question.