Multiagent Deep Reinforcement Learning-Aided Output Current Sharing Control for Input-Series Output-Parallel Dual Active Bridge Converter
Yu Zeng, Josep Pou, Changjiang Sun, Ali I. Maswood, Jiaxin Dong, Suvajit Mukherjee, Amit Kumar Gupta
Abstract
This letter proposes a multiagent soft actor-critic (MASAC) enabled multiagent deep reinforcement learning (MADRL) algorithm for output current sharing of the input-series output-parallel dual active bridge converter. The modular converter is partitioned into different submodules, which are treated as DRL agents of Markov games. Furthermore, all agents are executed decentralized to provide online control decisions after collaborative training. The proposed MASAC algorithm verified in a 150 V/50 V hardware prototype shows optimal dynamic performance.
Topics & Concepts
Reinforcement learningModular designComputer scienceDual (grammatical number)Bridge (graph theory)Series (stratigraphy)Control (management)Artificial intelligenceOperating systemBiologyInternal medicineArtMedicinePaleontologyLiteratureAdvancements in Semiconductor Devices and Circuit DesignSilicon Carbide Semiconductor TechnologiesMicrogrid Control and Optimization