Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques

TL;DR

This paper introduces a reinforcement learning approach using proximal policy optimization for controlling DC-DC boost converters, demonstrating superior performance over traditional control methods in simulation.

Contribution

It presents a novel PPO-based reinforcement learning control method for boost converters and compares its effectiveness against traditional control techniques.

Findings

PPO-based RL achieves shorter settling time and better stability.

RL control with PPO outperforms traditional methods in step response.

Simulation results confirm enhanced converter performance.

Abstract

This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods. The performance of the PPO algorithm is evaluated using MATLAB Simulink co-simulation, and the results demonstrate that the most efficient approach for achieving short settling time and stability is to combine the PPO algorithm with a reinforcement learning-based control method. The simulation results show that the control method based on RL with the PPO algorithm pro vides step response characteristics that outperform traditional control approaches, thereby enhancing DC-DC boost converter control. This research also highlights the inherent capability of the reinforcement learning method to enhance the performance of boost converter control.