Deep Reinforcement Learning-Aided Frequency Control of LCC-S Resonant Converters for Wireless Power Transfer Systems

TL;DR

This paper introduces a deep reinforcement learning approach using the TD3 algorithm to automatically tune controllers for LCC-S resonant converters, improving stability and robustness in wireless power transfer systems.

Contribution

It presents a novel DRL-based control strategy that replaces manual tuning with an automated, adaptive method for power converter regulation.

Findings

DRL-based tuning outperforms traditional PI controllers in stability

Enhanced robustness under varying operating conditions

Significant improvements in response time and control accuracy

Abstract

This paper presents a novel deep reinforcement learning (DRL)-based control strategy for achieving precise and robust output voltage regulation in LCC-S resonant converters, specifically designed for wireless power transfer applications. Unlike conventional methods that rely on manually tuned PI controllers or heuristic tuning approaches, our method leverages the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to systematically optimize PI controller parameters. The complex converter dynamics are captured using the Direct Piecewise Affine (DPWA) modeling technique, providing a structured approach to handling its nonlinearities. This integration not only eliminates the need for manual tuning, but also enhances control adaptability under varying operating conditions. The simulation and experimental results confirm that the proposed DRL-based tuning approach significantly outperforms traditional methods in terms of stability, robustness, and response time. This work demonstrates the potential of DRL in power electronic control, offering a scalable and data-driven alternative to conventional controller design approaches.