Research on Virtually Coupled Train Formation Control of Heavy-haul Train Based on Improved MPC under Moving Block System

被引:0
|
作者
Lin Z. [1 ]
Yu H. [1 ]
Tai G. [1 ]
Guo W. [2 ]
Shi Z. [3 ]
Yu L. [2 ]
Huang Y. [1 ,4 ]
机构
[1] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
[2] Shuohuang Railway Development Co., Ltd., Suning
[3] Traffic Control Technology, Co., Ltd., Beijing
[4] National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing
来源
关键词
cooperative control; heavy⁃haul train; model predictive control; virtually coupled train formation;
D O I
10.3969/j.issn.1001-8360.2024.02.009
中图分类号
学科分类号
摘要
Virtually coupled train formation control is an important means to improve the transport capacity of urban rail transit with moving block system. Aiming at the characteristics of heavy load, long formation and difficult control of heavy⁃haul train, this paper proposed a control method for virtually coupled heavy⁃haul train formation based on improved MPC (model predictive control) under moving block system. In this paper, with the establishment of the heavy⁃haul train formation model based on multi⁃particle model with distributed control structure, the MPC method was improved by adding adaptive feedback link, while the nonlinear function was processed by introducing 0⁃1 index variable to further solve the mixed integer quadratic optimization problem. The results of the simulation using the actual data of railway line and trains under Shuohuang heavy⁃haul moving block system show that the maximum speed error is 0. 465 km/ h, with the maximum interval error of 1. 899 m, and with the error distribution being concentrated near the zero point, proving that the improved MPC control method proposed in this paper, which can meet the requirements of virtually coupled heavy⁃haul train formation under moving block system, is effective. © 2024 Science Press. All rights reserved.
引用
收藏
页码:74 / 81
页数:7
相关论文
共 20 条
  • [1] LIANG Ziyue, YU Huazhen, TAI Guoxuan, Et al., Research on Optimization of Train Combination Strategy of Heavy⁃haul Railway Technical Station [J], Journal of the China Railway Society, 44, 4, pp. 9-18, (2022)
  • [2] CHEN Jiwei, Analysis of the Influence of Freight Train Speed, Density and Weight on Transport Capacity under Heavy Load Conditions [J], China Plant Engineering, 13, (2018)
  • [3] ZHAO Haijun, Research on Transport Capacity of Heavy Haul Railways in Moving Block Mode [J], Energy Science and Technology, 18, 8, (2020)
  • [4] People’s Information.Rapid Start and Stop, More Stable Seat Layout, More Spacious First⁃time Running Smart Train of Line 11
  • [5] LUO Xiaolin, TANG Tao, LIN Bingyue, Et al., A Robust Model Predictive Control Approach for Reducing Following Distance between Virtually Coupled Unit Trains [J], Journal of the China Railway Society, 45, 8, (2023)
  • [6] AOUN J, QUAGLIETTA E, GOVERDE R M P., Investigating Market Potentials and Operational Scenarios of Virtual Coupling Railway Signaling [J], Transportation Research Record, 2674, 8, (2020)
  • [7] PAN D, ZHENG Y P., Dynamic Control of High⁃speed Train Following Operation [J], PROMET⁃Traffic & Transportation, 26, 4, (2014)
  • [8] LIU L, WANG P, ZHANG B, Et al., Coordinated Control Method of Virtually Coupled Train Formation Based on Multi Agent System [M] ∥ Advances in Smart Vehicular Technology, Transportation, Communication and Applications, (2018)
  • [9] CHEN M L, XUN J, LIU Y F., A Coordinated Collision Mitigation Approach for Virtual Coupling Trains by Using Model Predictive Control [C], 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), (2020)
  • [10] FELEZ J, KIM Y, BORRELLI F., A Model Predictive Control Approach for Virtual Coupling in Railways, IEEE Transactions on Intelligent Transportation Systems, 20, 7, pp. 2728-2739, (2019)