Increasing railway line space utilization through virtual coupling techniques is a focal point of research in the field of rail transportation. To achieve coordinated trains entry at station with multiple arrival–departure tracks and prevent collisions among virtual coupling trains, this paper proposes a scale consistency-based method for train virtual coupling and coordinated operation control. By adjusting the scale consistency coefficient, this approach ensures safe train operations while achieving coordinated station entry. Furthermore, the stability of the high-speed train scale consistency system is proven using the Lyapunov functions, and simulations are conducted using real track data for verification. The results confirm that this control method effectively ensures safe control of the relative distances between trains, thereby improving the utilization of railway space.
CaoYWenJMaL (2021) Tracking and collision avoidance of virtual coupling train control system. Future Generation Computer Systems120: 76–90.
2.
CaoYZhangZChengF, et al. (2022) Trajectory optimization for high-speed trains via a mixed integer linear programming approach. IEEE Transactions on Intelligent Transportation Systems23(10): 17666–17676.
3.
ChenSZouZZhangZ, et al. (2023) Fixed-time scaled consensus of multi-agent systems with input delay. Journal of the Franklin Institute360(12): 8821–8840.
4.
DonganontMLiuX (2023) Scaled consensus problems of multi-agent systems via impulsive protocols. Applied Mathematical Modelling116: 532–546.
5.
HuangZWangPZhouF, et al. (2022) Cooperative tracking control of the multiple-high-speed trains system using a tunable artificial potential function. Journal of Advanced Transportation2022(1): 3639586.
KetphatNWhiteingALiuR (2022) State movement for controlling trains operating under the virtual coupling system. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit236(2): 172–182.
8.
LiMDengFRenH (2020) Scaled consensus of multi-agent systems with switching topologies and communication noises. Nonlinear Analysis: Hybrid Systems36: 100839.
9.
LiuYZhouYSuS, et al. (2021) An analytical optimal control approach for virtually coupled high-speed trains with local and string stability. Transportation Research Part C: Emerging Technologies125: 102886.
10.
LuoXLiuHZhangL, et al. (2021) A model predictive control based inter-station driving strategy for virtual coupling trains in railway system. In: 2021 IEEE international intelligent transportation systems conference (ITSC), Indianapolis, IN, 19–22 September, pp. 3927–3932. New York: IEEE.
11.
PanJPengQZhanS, et al. (2021) Multiscenario-based train headway analysis under virtual coupling system. Journal of Advanced Transportation. Epub ahead of print 21 December. DOI: 10.1155/2021/5642709.
12.
QuagliettaESpartalisPWangM, et al. (2022) Modelling and analysis of virtual coupling with dynamic safety margin considering risk factors in railway operations. Journal of Rail Transport Planning and Management22: 100313.
13.
SongHShangguanWShengZ, et al. (2021) Optimization method of dynamic trajectory for high-speed train group based on resilience adjustment. Journal of Traffic and Transportation Engineering21(4): 235–250.
14.
SuSLiuWZhuQ, et al. (2022) A cooperative collision-avoidance control methodology for virtual coupling trains. Accident Analysis and Prevention173: 106703.
15.
SuSSheJWangD, et al. (2023) A stabilized virtual coupling scheme for a train set with heterogeneous braking dynamics capability. Transportation Research Part C: Emerging Technologies146: 103947.
16.
SunYQiangHWangL, et al. (2023) A fuzzy-logic-system-based cooperative control for the multi-electromagnets suspension system of maglev trains with experimental verification. IEEE Transactions on Fuzzy Systems31(10): 3411–3422.
17.
SunYShiPLimCC (2022) Event-triggered sliding mode scaled consensus control for multi-agent systems. Journal of the Franklin Institute359(2): 981–998.
18.
WangQChaiMLiuH, et al. (2021) Optimized control of virtual coupling at junctions: A cooperative game-based approach. Actuators10(9): 207.
19.
XingZZhangZGuoJ, et al. (2023) Rail train operation energy-saving optimization based on improved brute-force search. Applied Energy330: 120345.
20.
XingZZhuJZhangZ, et al. (2022) Energy consumption optimization of tramway operation based on improved PSO algorithm. Energy258: 124848.
21.
XuRMengJLiD, et al. (2023) Energy-efficient optimization method of urban rail train based on following consistency. Energies16(4): 2050.
22.
ZhangZChenSZhaoX (2023) Finite-time scaled consensus tracking of a class of high-order nonlinear multi-agent systems with unstable linearization. Journal of the Franklin Institute360(3): 2000–2018.
23.
ZhangZSongHWangH, et al. (2021) Cooperative multi-scenario departure control for virtual coupling trains: A fixed-time approach. IEEE Transactions on Vehicular Technology70(9): 8545–8555.
24.
ZhouXLuFWangL (2022) Optimization of train operation planning with full-length and short-turn routes of virtual coupling trains. Applied Sciences12(15): 7935.
