Edge-Deployable Tabular Q-Learning Voltage Control for LLC Resonant Converters Under Dynamic Conditions

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.