ResDDQN-FPGA: A Reinforcement Learning Framework for Dynamic and Efficient Control in Resonant DC-DC Converters

This paper presents a novel DC-DC loop control framework, ResDDQN-FPGA, which integrates reinforcement learning with field-programmable gate arrays (FPGAs) to enhance the dynamic response and stability of LLC resonant DC-DC converters. Traditional control strategies for LLC converters face difficulties in adapting to variations in operating conditions, especially with aging circuit components. To address these issues, we introduce the Double DQN algorithm, modified with a residual structure to form the ResDDQN control algorithm, which provides accurate estimation of control objectives with reduced computational demand. The ResDDQN algorithm is deployed on an FPGA platform, leveraging its parallel processing capabilities to improve execution speed and efficiency, while also reducing resource usage and energy consumption. Experimental validation on a 1kW full-bridge LLC circuit demonstrates the framework’s effectiveness, achieving superior dynamic performance, lower energy consumption, and greater stability. This highly efficient and responsive control solution is well-suited for real-time applications, maintaining scalability without compromising computational demand.