高速列车精确停车的鲁棒自触发预测控制

刘晓宇; 荀径; 高士根; 阴佳腾

doi:10.16383/j.aas.c200039

高速列车精确停车的鲁棒自触发预测控制

doi: 10.16383/j.aas.c200039

1.
北京交通大学轨道交通控制与安全国家重点实验室北京 100044

基金项目: 国家自然科学基金(61790570, 61790573)资助

详细信息

作者简介:
刘晓宇：北京交通大学电子信息工程学院硕士研究生. 2017年获得北京交通大学学士学位. 主要研究方向为轨道交通运行优化与控制, 模型预测控制. E-mail: bjtulxy@bjtu.edu.cn

荀径：北京交通大学轨道交通控制与安全国家重点实验室副教授. 2012年获得北京交通大学博士学位. 2008年到2009年加州大学伯克利分校PATH访问学者. 主要研究方向为先进的列车控制方法, 铁路运输优化问题, 交通流理论, 元胞自动机和强化学习等. 本文通信作者. E-mail: jxun@bjtu.edu.cn

高士根：北京交通大学轨道交通控制与安全国家重点实验室副教授. 2016年获得北京交通大学电子信息工程学院博士学位. 主要研究方向为列车智能控制和多车协同优化. E-mail: sggao@bjtu.edu.cn

阴佳腾：北京交通大学轨道交通控制与安全国家重点实验室副教授. 2018年获得北京交通大学博士学位. 2015年至2016年威斯康星大学麦迪逊分校、2019年苏黎世联邦理工学院访问学者. 主要研究方向为轨道交通运行优化与控制, 最优控制, 机器学习与离散优化. E-mail: jtyin@bjtu.edu.cn

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出版历程
- 收稿日期: 2020-01-17
- 录用日期: 2020-05-15
- 网络出版日期: 2021-12-29
- 刊出日期: 2022-01-25

Robust Self-triggered Model Predictive Control for Accurate Stopping of High-speed Trains

1.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044

Funds: Supported by National Natural Science Foundation of China (61790570, 61790573)

More Information

Author Bio:
LIU Xiao-Yu　Master student at the School of Electronic and Information Engineering, Beijing Jiaotong University. He received his bachelor degree from Beijing Jiaotong University in 2017. His research interest covers optimization and control of railway systems, and model predictive control

XUN Jing　Associate professor at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. He received his Ph.D. degree from Beijing Jiaotong University in 2012. From 2008 to 2009, he was a visiting scholar with the PATH, University of California at Berkeley. His research interest covers advanced train control methods, optimization problem in rail transport, traffic flow theory, cellular automata, and reinforcement learning. Corresponding author of this paper

GAO Shi-Gen　Associate professor at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. He received his Ph.D. degree from Beijing Jiaotong University in 2016. His research interest covers intelligent train control and cooperative optimization of multiple trains

YIN Jia-Teng　Associate professor at the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. He received his Ph.D. degree from Beijing Jiaotong University in 2018. He was a visiting scholar at University of Wisconsin Madison (2015-2016) and Swiss Federal Institute of Technology Zurich (ETH) (2019). His research interest covers control and optimization of railway trains, optimal control, machine learning, and discrete optimization

摘要

摘要: 列车精确停车作为列车自动运行(Automatic train operation, ATO)系统的一项核心功能, 对高速列车的安全和高效运行至关重要. 本文针对高速列车停车过程的特点, 考虑在避免控制输出频繁切换的前提下实现高精度的停车曲线跟踪, 提出了基于模型预测控制(Model predictive control, MPC)的精确停车算法. 针对列车停车过程中外部不确定性阻力干扰, 采用鲁棒模型预测控制方法, 提高对外部干扰的鲁棒性. 引入自触发控制策略, 以进一步减少控制输出的频繁切换, 提高停车过程的舒适度. 该方法不需要每个采样时间都求解线性约束二次规划问题, 降低了对系统采样和通信能力的要求, 提高了算法的实用性. 分析结果表明, 高速列车精确停车控制方法的稳定性和性能指标的次优性可以得到保证. 基于高速列车实际运行数据的仿真结果验证了算法的有效性.
- 高速列车 /
- 列车自动运行 /
- 精确停车 /
- 模型预测控制 /
- 自触发控制
Abstract: Accurate stopping is a key technology of the automatic train operation (ATO) system and plays an important role in safe and efficient train operations. This paper presents a model predictive control (MPC) based train stop control method according to the characteristics of train stopping processes, which can realize high-precision speed tracking and avoid frequent switching of control output. Considering the uncertain disturbance from the external environment, a robust MPC method is adopted to improve the robustness for external disturbances. Then, a self-triggered control scheme is introduced to reduce the frequent switches of the control output. Furthermore, the control scheme does not require calculating the linear constraints quadratic programming problem at all times resulting in lower sampling and communication requirements. The stability and sub-optimality of the approach can be guaranteed. The simulation results based on the real-life operation data verify the effectiveness of the proposed method.
- High-speed train /
- automatic train operation (ATO) /
- accurate stopping /
- model predictive control (MPC) /
- self-triggered control

HTML全文

图 1 列车制动控制过程

Fig. 1 Diagram of train braking control process

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图 2 列车制动模型

Fig. 2 Train braking system model

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图 3 PID列车停车控制的速度−位置曲线

Fig. 3 PID speed-distance profile of train stopping

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图 4 MPC列车停车控制的速度−位置曲线

Fig. 4 MPC speed-distance profile of train stopping

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图 5 RMPC列车停车控制的速度−位置曲线 ($ N = 5 $)

Fig. 5 RMPC speed-distance profile of train stopping ($ N = 5 $)

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图 6 RMPC列车停车控制的速度−位置曲线 ($ N = 10 $)

Fig. 6 RMPC speed-distance profile of train stopping ($ N = 10 $)

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图 7 RMPC不同预测时域下的速度跟踪误差

Fig. 7 RMPC speed tracking error of different horizons

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图 8 RSMPC列车停车控制的速度−位置曲线 ($ N = 5 $)

Fig. 8 RSMPC speed-distance profile of train stopping ($ N = 5 $)

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图 9 RSMPC列车停车控制的速度−位置曲线 ($ N = 10 $)

Fig. 9 RSMPC speed-distance profile of train stopping ($ N = 10 $)

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图 10 RSMPC不同预测时域下的速度跟踪误差

Fig. 10 RSMPC speed tracking error of different horizons

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图 11 不同预测时域下RMPC和RSMPC的停车精度

Fig. 11 Stopping error of different prediction horizons with RMPC and RSMPC

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表 1 仿真参数设置

Table 1 The simulation parameters

参数	取值
最大制动加速度	−1.07 m/s²
列车重量	490 t
基本阻力	5.4 + 0.0098 v + 0.00163 v²
采样间隔T	0.2 s
制动模型时延T_d	1.0 s
制动模型时间常数$\tau$	0.4 s
制动起始点速度	20 m/s
制动起始点位置	0 m
停车点位置	400 m
限速	20 m/s
参考制动加速度	−0.5 m/s²

下载: 导出CSV

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