面向虚拟编组的高速列车鲁棒模型预测控制

付雅婷; 周坤; 李中奇

doi:10.19713/j.cnki.43-1423/u.T20251365

您当前的位置：

首页 >

文章列表页 >

面向虚拟编组的高速列车鲁棒模型预测控制

高速铁路技术 | 更新时间：2026-07-24

- 面向虚拟编组的高速列车鲁棒模型预测控制
- Robust model predictive control for high-speed trains in virtual formation
- 铁道科学与工程学报 2026年23卷第7期页码：3110-3122
- 作者机构：
  
  1.华东交通大学山区土木工程安全与韧性全国重点实验室，江西南昌 330013
  2.华东交通大学电气与自动化工程学院，江西南昌 330013
  3.华东交通大学江西省先进控制与优化重点实验室，江西南昌 330013
- 作者简介：
  
  付雅婷(1988—)，女，江西南昌人，教授，博士，从事列车运行过程建模与控制研究；E-mail：fuyating0103@163.com
- 基金信息：
  
  山区土木工程安全与韧性全国重点实验室自主课题(HJGZ2024209);国家自然科学基金资助项目(62363011;52472342);江西省自然科学基金资助项目(20242BAB26022);中国博士后科学基金资助项目(2024M760904)
- DOI：10.19713/j.cnki.43-1423/u.T20251365
  中图分类号： U266.2
- 收稿：2025-08-31，
  
  网络首发：2026-07-24，
  
  纸质出版：2026-07-28
- 稿件说明：
移动端阅览
付雅婷,周坤,李中奇.面向虚拟编组的高速列车鲁棒模型预测控制[J].铁道科学与工程学报,2026,23(07):3110-3122.

FU Yating,ZHOU Kun,LI Zhongqi.Robust model predictive control for high-speed trains in virtual formation[J].Journal of Railway Science and Engineering,2026,23(07):3110-3122.
付雅婷,周坤,李中奇.面向虚拟编组的高速列车鲁棒模型预测控制[J].铁道科学与工程学报,2026,23(07):3110-3122. DOI： 10.19713/j.cnki.43-1423/u.T20251365.

FU Yating,ZHOU Kun,LI Zhongqi.Robust model predictive control for high-speed trains in virtual formation[J].Journal of Railway Science and Engineering,2026,23(07):3110-3122. DOI： 10.19713/j.cnki.43-1423/u.T20251365.

摘要

为满足高速铁路日益增长的运输需求，提升高速铁路路网整体运输效率，聚焦虚拟编组运行模式高速列车的安全高效控制问题，以CRH380A型高速列车为研究对象，提出一种覆盖出站编组到解编进站全阶段的高速列车虚拟编组运行模式。为确保高速列车遵循既定模式运行，考虑列车在行驶过程中的非线性运行阻力、车厢间的相互作用力和外部未知扰动等因素，建立多列车的多质点动力学模型。据此，结合高速列车运行过程中的速度限制、运行间距限制和控制量限制等约束，设计优化运行目标函数，进而提出基于分层架构的鲁棒模型预测控制方法。求解层采用二次规划策略实时生成列车群组运行最优参考轨迹，控制层通过鲁棒模型预测控制器计算获得最优控制力，实现群组列车在虚拟编组新模式下的协同运行。仿真结果表明，相较于传统模型预测控制，在面对相同外界干扰时，所设计方法在列车速度跟踪误差、加速度波动范围、控制力输出方面均表现更优，列车的速度跟踪精度最高提升了47%，运行时的最大加速度最多降低了16.8%，控制力的变化幅度更小，证明该方法具备良好的抗干扰性与鲁棒性。同时，在面对突发干扰时，所提出的方法缩短了虚拟编组列车完成编组的时间，在紧急制动情况下也具有更好的稳定性。研究结果为进一步保障高速列车运行安全和提高高速铁路路网运输效率提供了参考。

Abstract

To address the growing transport demands of high-speed railways and enhance the overall operational efficiency of the high-speed network

this study focused on the safe and efficient control of high-speed trains operating under a virtual formation mode. Taking the CRH380A high-speed train as the research subject

a virtual formation operation mode covering the entire phase from departure formation to disassembly upon arrival was proposed. To ensure adherence to this mode

a multi-train

multi-mass dynamics model was established

accounting for non-linear operational resistance during train movement

inter-carriage interactions

and external unknown disturbances. Based on this

an optimized operational objective function was designed incorporating constraints such as speed limitations

operational spacing restrictions

and control quantity limitations during high-speed train operations. Subsequently

a robust model predictive control method based on a hierarchical architecture was proposed. The solution layer employed a quadratic programming strategy to generate optimal reference trajectories for train group operations in real time

while the control layer calculates optimal control forced through a robust model predictive controller

enabling coordinated operation of train groups under the new virtual formation mode. Simulation results demonstrate that

compared to traditional model predictive control

the proposed method can exhibit superior performance in train speed tracking error

acceleration fluctuation range

and control force output when subjected to identical external disturbances. Speed tracking accuracy can improve by up to 47%

maximum operational acceleration decreased by up to 16.8%. The control force variation is reduced

confirming the method’s robust disturbance rejection capability. Furthermore

when encountering sudden disturbances

the proposed method can reduce the time required for virtual formation trains to complete marshalling. It also exhibits superior stability during emergency braking scenarios. These findings can provide valuable insights for enhancing the operational safety of high-speed trains and improving the transport efficiency of high-speed rail networks.

关键词

Keywords

references

DI MEO C , DI VAIO M , FLAMMINI F , et al . ERTMS/ETCS virtual coupling: proof of concept and numerical analysis [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 6 ): 2545 - 2556 .

宋展鹏 , 田小鹏 , 牛惠民 , 等 . 灵活编组条件下城市轨道交通列车时刻表与车底运用综合优化 [J ] . 铁道科学与工程学报 , 2025 , 22 ( 7 ): 2960 - 2972 .

SONG Zhanpeng , TIAN Xiaopeng , NIU Huimin , et al . Integrated optimization of train timetable and rolling stock circulation with flexible composition strategies for an urban rail transit line [J ] . Journal of Railway Science and Engineering , 2025 , 22 ( 7 ): 2960 - 2972 .

帅斌 , 罗佳楠 , 冯心妍 , 等 . 虚拟编组下基于跟驰模型的列车群运行控制方法研究 [J ] . 交通运输系统工程与信息 , 2024 , 24 ( 3 ): 1 - 11 .

SHUAI Bin , LUO Jianan , FENG Xinyan , et al . A train group control method based on car following model under virtual coupling [J ] . Journal of Transportation Systems Engineering and Information Technology , 2024 , 24 ( 3 ): 1 - 11 .

ZHANG Zixuan , SONG Haifeng , WANG Hongwei , et al . Cooperative multi-scenario departure control for virtual coupling trains: a fixed-time approach [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 9 ): 8545 - 8555 .

朱才梵 , 王悉 , 杨欣 , 等 . 基于鲁棒分层控制的虚拟编组列车动态解编方法 [J ] . 交通运输工程与信息学报 , 2025 , 23 ( 2 ): 150 - 160 .

ZHU Caifan , WANG Xi , YANG Xin , et al . Dynamic deprogramming approach for virtual coupled trains based on robust hierarchical control [J ] . Journal of Transportation Engineering and Information , 2025 , 23 ( 2 ): 150 - 160 .

LI Yike , TONG Yin , GIUA A . Safe optimal train formation control in virtual coupling using control barrier functions [C ] // 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) , August 28-September 1, 2024 , Bari , Italy . IEEE , 2024 : 647 - 652 .

LIU Yafei , LIU Ronghui , WEI Chongfeng , et al . Distributed model predictive control strategy for constrained high-speed virtually coupled train set [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 1 ): 171 - 183 .

侯涛 , 唐丽 , 牛宏侠 . 基于数据驱动的高速列车速度复合控制研究 [J ] . 交通运输系统工程与信息 , 2023 , 23 ( 3 ): 145 - 152 .

HOU Tao , TANG Li , NIU Hongxia . Data-driven speed compound control of high-speed train [J ] . Journal of Transportation Systems Engineering and Information Technology , 2023 , 23 ( 3 ): 145 - 152 .

林俊亭 , 倪铭君 . 基于扩张状态观测器的虚拟编组触发模型预测控制 [J ] . 交通运输系统工程与信息 , 2023 , 23 ( 4 ): 134 - 146 .

LIN Junting , NI Mingjun . Trigger model predictive control based on extended state observers for virtual coupling [J ] . Journal of Transportation Systems Engineering and Information Technology , 2023 , 23 ( 4 ): 134 - 146 .

LUO Xiaolin , LIU Hongjie , ZHANG Lei , et al . A model predictive control based inter-station driving strategy for virtual coupling trains in railway system [C ] // 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) , September 19-22, 2021 , Indianapolis, IN , USA . IEEE , 2021 : 3927 - 3932 .

FELEZ J , KIM Y , BORRELLI F . A model predictive control approach for virtual coupling in railways [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 7 ): 2728 - 2739 .

韩京清 . 从PID技术到“自抗扰控制”技术 [J ] . 控制工程 , 2002 , 9 ( 3 ): 13 - 18 .

HAN Jingqing . From PID technique to active disturbances rejection control technique [J ] . Basic Automation , 2002 , 9 ( 3 ): 13 - 18 .

WANG Jianhua , WANG Yiling , WU Xialai , et al . Robust MPC for systems with model uncertainties and measurement outliers [J ] . IEEE Access , 2022 , 10 : 119028 - 119039 .

LUO Xiaolin , TANG Tao , LU Xiaoyu , et al . Robust constraint satisfaction and stability of virtually coupled train set with uncertain dynamics: a dual-mode robust MPC approach [J ] . Transportation Research Part C: Emerging Technologies , 2023 , 156 : 104356 .

LUO Xiaolin , TANG Tao , CHAI Ming , et al . A hierarchical MPC approach for arriving-phase operation of virtually coupled train set [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 7 ): 7237 - 7249 .

LIU Yafei , ZHOU Yang , SU Shuai , et al . Control strategy for stable formation of high-speed virtually coupled trains with disturbances and delays [J ] . Computer-Aided Civil and Infrastructure Engineering , 2023 , 38 ( 5 ): 621 - 639 .

李柯颖 , 王小敏 , 邓红元 , 等 . 基于扩张状态观测器的虚拟编组列车分布式鲁棒控制 [J ] . 铁道科学与工程学报 , 2026 , 23 ( 2 ): 616 - 630 .

LI Keying , WANG Xiaomin , DENG Hongyuan , et al . Robust control of virtual coupled trains based on the extended state observer [J ] . Journal of Railway Science and Engineering , 2026 , 23 ( 2 ): 616 - 630 .

李中奇 , 周靓 , 杨辉 . 高速动车组数据驱动无模型自适应积分滑模预测控制 [J ] . 自动化学报 , 2024 , 50 ( 1 ): 194 - 210 .

LI Zhongqi , ZHOU Liang , YANG Hui . Data-driven model-free adaptive integral sliding mode predictive control for high-speed electric multiple unit [J ] . Acta Automatica Sinica , 2024 , 50 ( 1 ): 194 - 210 .

侯涛 , 郭洋洋 , 陈昱 , 等 . 基于多质点模型的高速列车速度控制研究 [J ] . 铁道科学与工程学报 , 2020 , 17 ( 2 ): 314 - 325 .

HOU Tao , GUO Yangyang , CHEN Yu , et al . Study on speed control of high-speed train based on multi-point model [J ] . Journal of Railway Science and Engineering , 2020 , 17 ( 2 ): 314 - 325 .

FENG Shuo , ZHANG Yi , LI S E , et al . String stability for vehicular platoon control: Definitions and analysis methods [J ] . Annual Reviews in Control , 2019 , 47 : 81 - 97 .

RAKOVIC S V , KERRIGAN E C , KOURAMAS K I , et al . Invariant approximations of the minimal robust positively Invariant set [J ] . IEEE Transactions on Automatic Control , 2005 , 50 ( 3 ): 406 - 410 .

KOLMANOVSKY I , GILBERT E G . Maximal output admissible sets for discrete-time systems with disturbance inputs [C ] // Proceedings of 1995 American Control Conference-ACC’95 , June 21-23, 1995 , Seattle, WA , USA . IEEE , 2002 : 1995 - 1999 .

ZHANG Kunpeng , JIANG Bin , CHEN Fuyang . Multiple-model-based diagnosis of multiple faults with high-speed train applications using second-level adaptation [J ] . IEEE Transactions on Industrial Electronics , 2021 , 68 ( 7 ): 6257 - 6266 .

YANG Hui , FU Yating , WANG Dianhui . Multi-ANFIS model based synchronous tracking control of high-speed electric multiple unit [J ] . IEEE Transactions on Fuzzy Systems , 2018 , 26 ( 3 ): 1472 - 1484 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于自适应反步滑模的磁悬浮系统非线性控制方法研究

一种基于预测误差校正的永磁同步电机模型预测电流控制方法

基于LADRC的悬浮控制优化研究

医疗废物运输网络渗流风险鲁棒性建模与仿真

基于Transformer的引力场导向铁路线路优化方法