Enactment of Deep Reinforcement Learning Agent for Duty Ratio Control of Dual Active Bridge Converter

This paper presents a deep reinforcement learning (DRL) agent for duty ratio control of the dual active bridge (DAB) converter using triple phase shift (TPS) modulation. The operation of a dual active bridge under the TPS modulation technique was briefly discussed, stating its advantage over the other phase shift techniques, thus creating the requirement of a model-free control approach for the converter. The reinforcement learning method has evolved as the deep reinforcement learning method that can solve complex problems with rising computational accuracy. This paper focuses on a deep deterministic policy gradient (DDPG) agent founded on actor-critic policy, i.e. implemented as the DRL controller. A detailed discussion about the control strategy using the DRL controller was presented. The model was simulated using MATLAB/Simulink software version 2024b to verify the voltage regulation of the dual active bridge using the DDPG agent. Finally, the conclusion presents the advantages of using a DRL controller for the TPS modulation of the DAB converter.