A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy
文献类型：期刊论文
| 作者 | Su, Shuai1,2; Zhu, Qingyang1,2; Liu, Junqing1,2; Tang, Tao1,2; Wei, Qinglai3,4 ; Cao, Yuan1,2
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2023-07-01 |
| 卷号 | 19期号:7页码:7885-7893 |
| ISSN号 | 1551-3203 |
| 关键词 | Deep reinforcement learning (RL) driving strategy energy-efficient train control (EETC) soft actorcritic (SAC) |
| DOI | 10.1109/TII.2022.3195888 |
| 通讯作者 | Cao, Yuan(ycao@bjtu.edu.cn) |
| 英文摘要 | The energy-efficient train control (EETC) problem is investigated in this article. And a soft actor-critic (SAC)-based method is proposed to optimize the train driving strategy. First, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process, which can be solved by deep reinforcement learning algorithms. Second, an optimization method based on the SAC method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals. |
| WOS关键词 | SYSTEM ; SUBWAY ; OPTIMIZATION |
| 资助项目 | National Natural Science Foundation of China[52172322] ; National Natural Science Foundation of China[U22A2046] ; Beijing Natural Science Foundation[L191015] ; Beijing Natural Science Foundation[L201004] ; State Key Laboratory of Rail Traffic Control and Safety[RCS2022ZZ003] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001020261500004 |
| 资助机构 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; State Key Laboratory of Rail Traffic Control and Safety |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53798]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Cao, Yuan |
| 作者单位 | 1.Beijing Jiaotong Univ, Frontiers Sci Ctr Smart High speed Railway Syst, Beijing 100044, Peoples R China 2.Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Inst Automat, Chinese Acad Sci, Sch Artificial Intelligence,Key Lab Management & C, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Shuai,Zhu, Qingyang,Liu, Junqing,et al. A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2023,19(7):7885-7893. |
| APA | Su, Shuai,Zhu, Qingyang,Liu, Junqing,Tang, Tao,Wei, Qinglai,&Cao, Yuan.(2023).A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,19(7),7885-7893. |
| MLA | Su, Shuai,et al."A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19.7(2023):7885-7893. |
入库方式： OAI收割
来源：自动化研究所
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