The Voltage Regulation of Boost Converters via a Hybrid DQN-PI Control Strategy Under Large-Signal Disturbances

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model.