Implementation of Transferring Reinforcement Learning for DC–DC Buck Converter Control via Duty Ratio Mapping

The reinforcement learning (RL) control approach with application to power electronics systems has become an emerging topic, while the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline-trained RL control strategies may sustain unexpected hurdles in practical implementation during the transfer procedure. In this article, a transfer methodology via a delicately designed duty ratio mapping is proposed for a dc–dc buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning controller. As the main contribution of this article, the proposed methodology is able to endow the control system to achieve: 1) voltage regulation and 2) adaptability and optimization abilities in the presence of uncertain circuit parameters and various working conditions. The feasibility and efficacy of the proposed methodology are demonstrated by comparative experimental studies.