Multiagent Deep Reinforcement Learning-Aided Output Current Sharing Control for Input-Series Output-Parallel Dual Active Bridge Converter

被引：14

作者：

Zeng, Yu ^{[1
]}

Pou, Josep ^{[1
]}

Sun, Changjiang ^{[2
]}

Maswood, Ali I. ^{[1
]}

Dong, Jiaxin ^{[1
]}

Mukherjee, Suvajit ^{[3
]}

Gupta, Amit Kumar ^{[3
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] Nanyang Technol Univ, Rolls Royce NTU Corp Lab, Singapore 639798, Singapore

[3] Rolls Royce Singapore Private Ltd, Singapore 638673, Singapore

来源：

IEEE TRANSACTIONS ON POWER ELECTRONICS | 2022年 / 37卷 / 11期

基金：

新加坡国家研究基金会;

关键词：

Heuristic algorithms; Voltage control; Training; Regulators; Vehicle dynamics; Complexity theory; Bridge circuits; Input-series output-parallel dual active bridge (ISOP-DAB) converter; multiagent soft actor-critic (MASAC); output current sharing (OCS); CONTROL STRATEGY; SCHEME;

D O I：

10.1109/TPEL.2022.3181243

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：12955 / 12961

页数：7

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