Intelligent train control for cooperative train formation: A deep reinforcement learning approach

被引：7
作者：
Zhang, Danyang [1 ]
Zhao, Junhui [1 ,2 ]
Zhang, Yang [1 ]
Zhang, Qingmiao [2 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[2] East China Jiaotong Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China
来源：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING | 2022年 / 236卷 / 05期
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
Cooperative train formation; intelligent train control; train-to-train communication; deep Q-learning; neural networks; COMMUNICATION; TRACKING; SYSTEMS;
D O I：
10.1177/09596518211064799
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Considering the intelligent train control problem in long-term evolution for metro system, a new train-to-train communication-based train control system is proposed, where the cooperative train formation technology is introduced for realizing a more flexible train operation mode. To break the limitation of centralized train control, a pre-exploration-based two-stage deep Q-learning algorithm is adopted in the cooperative train formation, which is one of the first intelligent approaches for urban railway formation control. In addition, a comfort-considered algorithm is given, where optimization measures are taken for providing superior passenger experience. The simulation results illustrate that the optimized algorithm has a smoother jerk curve during the train control process, and the passenger comfort can be improved. Furthermore, the proposed algorithm can effectively accomplish the train control task in the multi-train tracking scenarios, and meet the control requirements of the cooperative formation system.
引用
收藏
页码：975 / 988
页数：14
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