Work

Proximal policy optimization-based reinforcement learning approach for DC-DC boost converter control: A comparative evaluation against traditional control techniques

Year: 2024

Type: article

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 provides 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.

Source: Heliyon

DOI: 10.1016/j.heliyon.2024.e37823

Authors: Utsab Saha, Atik Jawad, Shakib Shahria, A.B.M Harun-Ur Rashid

Institutions: Bangladesh University of Engineering and Technology, BRAC University, University of Liberal Arts Bangladesh

Language: English

Topic: Advanced DC-DC Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,100