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2. National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
3. Department of Research Institute, Traffic Control Technology Co., Ltd., Beijing 100070, China
4. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China, fund=null, authors=LIU Hongjie1,2, LANG Yinghui1, ZHANG Lei1,3, TANG Tao4, authorsList=LIU Hongjie, LANG Yinghui, ZHANG Lei, TANG Tao), CN=ArticleExt(id=1242140470411866952, articleId=1242140467312271651, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于协同控制的虚拟编组列车参考曲线生成方法, columnId=1242140469916934445, journalTitle=科技导报, columnName=专题:先进列控技术, runingTitle=null, highlight=null, articleAbstract=面向虚拟编组在城市轨道交通中的应用需求,提出了对虚拟编组列车运行具有重要影响的停站时间不同步问题,并定义了停站时间差的计算方式;在分析相关领域多智能体协同控制方法的基础上,提出了基于信息汇总集中决策型协同控制方式的虚拟编组列车参考曲线生成方法;构建了虚拟编组列车参考曲线生成问题的数学优化模型,并设计了基于强化学习DQN(deep Q network)的求解方法,在考虑全局优化目标的基础上为每一个列车单元生成各自的参考曲线,指导各列车单元协同运行控制实现虚拟编组列车整体运行的目标;基于北京地铁11号线真实数据进行了数值仿真实验并与既有文献中的虚拟编组列车运行控制方式进行了对比,实验结果验证了所提基于协同控制的虚拟编组列车参考曲线生成方法的有效性。, correspAuthors=null, authorNote=刘宏杰,副研究员,研究方向为列控系统设计、集成与优化,电子信箱:hjliu2@bjtu.edu.cn, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=zXHBSOZmMMb6fp8DI0t2kw==, pdfFileSize=3597265, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 北京交通大学电子信息工程学院,北京 100044
2. 北京交通大学轨道交通运行控制系统国家工程研究中心,北京 100044
3. 交控科技股份有限公司交控研究院,北京 100070
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基于协同控制的虚拟编组列车参考曲线生成方法
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科技导报 | 专题:先进列控技术 2023,41(10): 62-72
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科技导报 | 专题:先进列控技术 2023, 41(10): 62-72
基于协同控制的虚拟编组列车参考曲线生成方法
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刘宏杰1,2,郎颖辉1,张蕾1,3,唐涛4
作者信息
    1. 北京交通大学电子信息工程学院,北京 100044
    2. 北京交通大学轨道交通运行控制系统国家工程研究中心,北京 100044
    3. 交控科技股份有限公司交控研究院,北京 100070
    4. 北京交通大学轨道交通控制与安全国家重点实验室,北京 100044
A cooperative control based reference curve generating method for virtually coupled train sets
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出版时间: 2023-05-28 doi: 10.3981/j.issn.1000-7857.2023.10.005
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面向虚拟编组在城市轨道交通中的应用需求,提出了对虚拟编组列车运行具有重要影响的停站时间不同步问题,并定义了停站时间差的计算方式;在分析相关领域多智能体协同控制方法的基础上,提出了基于信息汇总集中决策型协同控制方式的虚拟编组列车参考曲线生成方法;构建了虚拟编组列车参考曲线生成问题的数学优化模型,并设计了基于强化学习DQN(deep Q network)的求解方法,在考虑全局优化目标的基础上为每一个列车单元生成各自的参考曲线,指导各列车单元协同运行控制实现虚拟编组列车整体运行的目标;基于北京地铁11号线真实数据进行了数值仿真实验并与既有文献中的虚拟编组列车运行控制方式进行了对比,实验结果验证了所提基于协同控制的虚拟编组列车参考曲线生成方法的有效性。
城市轨道交通  /  虚拟编组列车  /  协同控制  /  参考曲线  /  强化学习
Virtual Coupling has been a hot research topic in railways in recent years. The proposed control methods in the existing literature for virtually coupled train sets (VCTS) mainly try to operate train units with the constraint of maintaining a small gap distance, but pay insufficient attention to the needs in the entering station process, which affects the work of VCTS in a station and is thus one of the most important part of VCTS in unban rail transits. To fill this gap, this paper firstly proposes an unsynchronized stopping problem between the train units in a VCTS when entering a station, defines the concept and the calculation method of stopping time difference between the train units. Then, inspired by the multi-agent cooperative control methods in related fields, a cooperative control-based reference curve generating method for VCTS is proposed. Followed by it, a mathematical optimization model to generate the reference curves for the train units in a VCTS is formulated, and a reinforcement learning DQN (deep Q network) based algorithm is designed to solve this problem. Optimized reference curves for each train units are generated automatically, considering the global objects and all constraints of VCTS. Finally, based on the real data of trains and lines from Beijing Metro Line 11, numerical simulation experiments are conducted. Experimental results prove the effectiveness of the proposed method which outperforms a commonly used control method of VCTS in the literature.
urban rail transit  /  virtually coupled train sets  /  cooperative control  /  reference curve  /  reinforcement learning
刘宏杰,郎颖辉,张蕾,唐涛. 基于协同控制的虚拟编组列车参考曲线生成方法. 科技导报, 2023 , 41 (10) : 62 -72 . DOI: 10.3981/j.issn.1000-7857.2023.10.005
LIU Hongjie, LANG Yinghui, ZHANG Lei, TANG Tao. A cooperative control based reference curve generating method for virtually coupled train sets[J]. Science & Technology Review, 2023 , 41 (10) : 62 -72 . DOI: 10.3981/j.issn.1000-7857.2023.10.005
2023年第41卷第10期
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doi: 10.3981/j.issn.1000-7857.2023.10.005
  • 接收时间:2022-12-27
  • 首发时间:2023-06-26
  • 出版时间:2023-05-28
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  • 收稿日期:2022-12-27
  • 修回日期:2023-03-15
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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