基于事件触发预测控制的高速列车轨迹跟踪

金文斌; 张佳辉; 姚柏伶; 孙鹏飞; 王青元

doi:10.13890/j.issn.1000-128X.2024.04.018

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基于事件触发预测控制的高速列车轨迹跟踪

电力牵引与控制 | 更新时间：2024-09-12

- 基于事件触发预测控制的高速列车轨迹跟踪
- High-speed train trajectory tracking based on event-triggered predictive control
- 机车电传动 2024年第4期页码：148-154
- 作者机构：
  
  1.中车长春轨道客车股份有限公司，吉林长春 130062
  2.西南交通大学电气工程学院，四川成都 610031
- 作者简介：
  
  张佳辉，男，硕士，研究方向为列车运行优化控制；E-mail: zhangjiahui965@163.com
- 基金信息：
  
  中国中车科研计划项目重点课题(2023CCB71)
- DOI：10.13890/j.issn.1000-128X.2024.04.018
  中图分类号： U292.91⁺4
- 纸质出版日期：2024-07-10，
  
  收稿日期：2024-03-20，
  
  修回日期：2024-07-01，
- 稿件说明：
移动端阅览
金文斌, 张佳辉, 姚柏伶, 等. 基于事件触发预测控制的高速列车轨迹跟踪[J]. 机车电传动, 2024(4): 148-154.

JIN Wenbin, ZHANG Jiahui, YAO Bailing, et al. High-speed train trajectory tracking based on event-triggered predictive control[J]. Electric drive for locomotives,2024(4): 148-154.
金文斌, 张佳辉, 姚柏伶, 等. 基于事件触发预测控制的高速列车轨迹跟踪[J]. 机车电传动, 2024(4): 148-154. DOI：10.13890/j.issn.1000-128X.2024.04.018.

JIN Wenbin, ZHANG Jiahui, YAO Bailing, et al. High-speed train trajectory tracking based on event-triggered predictive control[J]. Electric drive for locomotives,2024(4): 148-154. DOI：10.13890/j.issn.1000-128X.2024.04.018.

摘要

高速列车运行环境的开放性导致列车容易受到外部阻力的影响，而该阻力难以通过建模的形式进行描述，为此文章研究了以“安全、平稳、准点”为跟踪目标的列车模型预测跟踪控制策略。首先，搭建了高速列车带约束、多目标预测控制模型，计算系统的Tube不变集，对列车状态和控制力进行紧束；在此基础上，为降低在线求解最优控制问题的计算量，设计了动态事件触发Tube模型预测控制策略，仅在满足触发条件时求解最优控制序列；最后，基于上海市域列车实际线路开展仿真验证，结果表明文章提出的控制策略可在保证较好跟踪性能的同时，显著减小模型预测控制的触发次数，降低在线计算量。

Abstract

The open operating environment of high-speed trains exposes them to disturbances from external resistances

which are difficult to characterize through modeling. To address this issue

this paper investigates a train model predictive tracking control (TMPC) strategy

with safety

ride comfort and punctuality as the tracking objectives. Firstly

a multi-objective predictive control model with constraints was constructed for high-speed trains

and the Tube invariant set of the system was calculated to tightly constrain train states and control forces. On this basis

in order to reduce the computational workload for solving the optimal control online

a dynamic event-triggered Tube model predictive control (DETMPC) strategy was designed

enabling the optimal control sequence to be solved only when the triggering conditions were satisfied. Furthermore

simulation verification was carried out using Shanghai urban trains running on real tracks. The results show that the control strategy proposed in this paper not only guarantee good tracking performance

but also reduce the frequencies of triggering the model predictive control (MPC)

lowering the workload of online calculations significantly.

关键词

高速列车列车自动驾驶轨迹跟踪模型预测控制事件触发控制

Keywords

high-speed trainautomatic train operationtrajectory trackingmodel predictive controlevent-triggered control

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