Cooperative control for multiple train systems: Self-adjusting zones, collision avoidance and constraints

被引:32
作者
Lin, Peng [1 ]
Tian, Yu [1 ]
Gui, Gui [2 ]
Yang, Chunhua [1 ]
机构
[1] Cent South Univ, Sch Automat, Changsha, Peoples R China
[2] Dept Transport Modelling, WSP Grp, Cambridge, England
基金
中国国家自然科学基金;
关键词
Cooperative control; Multiple train systems; Constraints; Self-adjusting zone; Collision avoidance; HIGH-SPEED TRAINS; CRUISE CONTROL; TRACKING CONTROL; OPTIMIZATION;
D O I
10.1016/j.automatica.2022.110470
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In practical multiple train systems, the desired relative positions of adjacent trains are usually not fixed values but some certain zones which are called "self-adjusting zones " in this paper. However, most of the existing related works were concerned about the case of fixed values and few works focused on the more practical case when the desired relative positions are some certain zones. The aim of this paper is to investigate cooperative control for multiple train systems under moving block system by taking into account self-adjusting zones, collision avoidance and constraints simultaneously, where each train needs not to coordinate with other trains in its desired self-adjusting zones and can be self-adjusted freely. A distributed cooperative control algorithm is proposed to enable all trains to track the desired velocity and operate steadily in their self-adjusting zones by only utilizing the local information of neighbor trains, where a switching mechanism is introduced to ensure the trains to avoid collision. The analysis is performed mainly based on the properties of stochastic matrices and multiple model transformations. It is shown that the braking process can be equivalent to a special coordination process, and the braking process, the coordination process and the self-adjusting process can be unified as an integrated whole to be analyzed by using the properties of stochastic matrices. By addressing the state interactions of the train equivalent system under different scenarios, it is proved that all relative positions of adjacent trains converge into desired self-adjusting zones without the occurrence of collision, while all trains finally move in a desired velocity. Numerical examples are included to illustrate the obtained results. (C) 2022 Elsevier Ltd. All rights reserved.
引用
收藏
页数:10
相关论文
共 28 条
[1]  
[Anonymous], 2008, GRAPH THEORY
[2]   Distributed Cooperative Cruise Control of Multiple High-Speed Trains Under a State-Dependent Information Transmission Topology [J].
Bai, Weiqi ;
Lin, Zongli ;
Dong, Hairong ;
Ning, Bin .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (07) :2750-2763
[3]   Cooperative Control Synthesis and Stability Analysis of Multiple Trains Under Moving Signaling Systems [J].
Dong, Hairong ;
Gao, Shigen ;
Ning, Bin .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (10) :2730-2738
[4]   Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios [J].
Funke, Joseph ;
Brown, Matthew ;
Erlien, Stephen M. ;
Gerdes, J. Christian .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2017, 25 (04) :1204-1216
[5]   Cooperative Prescribed Performance Tracking Control for Multiple High-Speed Trains in Moving Block Signaling System [J].
Gao, Shigen ;
Dong, Hairong ;
Ning, Bin ;
Zhang, Qi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (07) :2740-2749
[6]  
Horn R. A., 1985, Matrix Analysis
[7]   Local energy minimization in optimal train control [J].
Howlett, P. G. ;
Pudney, P. J. ;
Vu, Xuan .
AUTOMATICA, 2009, 45 (11) :2692-2698
[8]   Adaptive Iterative Learning Control for High-Speed Trains With Unknown Speed Delays and Input Saturations [J].
Ji, Honghai ;
Hou, Zhongsheng ;
Zhang, Ruikun .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2016, 13 (01) :260-273
[9]  
Leonard NE, 2001, IEEE DECIS CONTR P, P2968, DOI 10.1109/CDC.2001.980728
[10]   Distributed optimal control for multiple high-speed train movement: An alternating direction method of multipliers [J].
Li, Shukai ;
Yang, Lixing ;
Gao, Ziyou .
AUTOMATICA, 2020, 112