Deep reinforcement learning-based proportional–integral control for dual-active-bridge converter

Due to the wide zero-voltage-switching range and low power losses, triple-phase-shift (TPS) modulation is commonly utilized in dual-active-bridge (DAB) converters. However, it is difficult to model it and design its controller for the reasons of model uncertainties and nonlinearity. In this paper, a deep reinforcement learning (DRL)-supervised proportional–integral (PI) control algorithm is proposed. The PI controller is used as a base controller to stabilize the output voltage of the DAB converter. In order to improve the control accuracy and the dynamic performance, the PI parameters are tuned by DRL. Besides, all operation modes of the TPS are learned during the training process. Thus, the operation mode with maximum power efficiency can be selected under a wide operation range. The simulation comparison results demonstrate the efficacy and superiorities of the proposed method.