Deep Reinforcement Learning-Based Control Strategy for Boost Converter

Authors

Yuxuan Dai, College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;Follow
Chenggang Cui, College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;Follow

Abstract

Abstract: In view of the problems of model uncertainty and nonlinearity in bus voltage stability control of Boost converter, an intelligent control strategy based on model-free deep reinforcement learning(RL) is proposed. RL double DQN(DDQN) algorithm and deep deterministic policy gradient(DDPG) algorithm are used, and the Boost converter controller is designed. The state, action space, reward function, and neural network are also designed to improve the dynamic performance of the controller. The joint simulation of the Boost converter model and RL agent is realized by RL modelica(RLM), a toolkit developed based on ModelicaGym. The proposed controller is compared with the double-loop PI controller, and the simulation shows that the bus voltage stability control based on the RL controller has better dynamic performance under three working conditions.

Recommended Citation

Dai, Yuxuan and Cui, Chenggang (2023) "Deep Reinforcement Learning-Based Control Strategy for Boost Converter," Journal of System Simulation: Vol. 35: Iss. 5, Article 18.
DOI: 10.16182/j.issn1004731x.joss.22-0101
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss5/18

First Page

1109

Revised Date

2022-03-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.22-0101

Last Page

1119

CLC

TP391.9

Recommended Citation

Yuxuan Dai, Chenggang Cui. Deep Reinforcement Learning-Based Control Strategy for Boost Converter[J]. Journal of System Simulation, 2023, 35(5): 1109-1119.

Corresponding Author

Chenggang Cui,cgcui@shiep.edu.cn

DOI

10.16182/j.issn1004731x.joss.22-0101