| |
Reinforcement Learning-based Control of a Buck Converter: A Comparative Study of DQN and DDPG Algorithms
| [View]
[Download]
|
| Author(s) |
Shima SHAHNOOSHI, Parisa RANJBARAN, Javad EBRAHIMI, Alireza BAKHSHAI, Praveen JAIN |
| Abstract |
Reinforcement learning (RL) has emerged as apromising approach for controlling powerelectronics systems due to its ability to controlnonlinear systems without accurate models, andhandle unknown changes. This paper presents acomparative study of two widely used deep RLalgorithms, Deep Deterministic Policy Gradient(DDPG) and Deep Q-Network (DQN), forcontrolling the output voltage of DC/DC buckconverters. DDPG and DQN are tailored tohandle continuous and discrete environments,respectively. The performance of thesealgorithms is evaluated through simulations withsteady-state error, overshoot, settling time, andtraining time serving as evaluation metrics. Thesimulation results demonstrate that both DDPGand DQN show excellent performance incontrolling power converters, with DDPGgenerally performing better than DQN in systemswith unpredictable changes. |
| Download |
| Filename: | 0546-epe2023-full-13350552.pdf |
| Filesize: | 1.024 MB |
|
| Type |
Members Only |
| Date |
Last modified 2023-09-24 by System |
| |
 |