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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

刘晓宇, 荀径, 高士根, 阴佳腾. 高速列车精确停车的鲁棒自触发预测控制. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200039
引用本文: 刘晓宇, 荀径, 高士根, 阴佳腾. 高速列车精确停车的鲁棒自触发预测控制. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200039
Liu Xiao-Yu, Xun Jing, Gao Shi-Gen, Yin Jia-Teng. Robust self-triggered model predictive control for accurate stopping of high-speed trains. Acta Automatica Sinica, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200039
Citation: Liu Xiao-Yu, Xun Jing, Gao Shi-Gen, Yin Jia-Teng. Robust self-triggered model predictive control for accurate stopping of high-speed trains. Acta Automatica Sinica, 2020, 46(x): 1−11 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
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等.E-mail: jtyin@bjtu.edu.cn

    通讯作者:

    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

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

Funds: Supported by National Natural Science Foundation of China (61790570, 61790573)
More Information
    Corresponding author: 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
  • 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.
  • 图  1  列车制动控制过程

    Fig.  1  Diagram of train braking control process

    图  2  列车制动模型

    Fig.  2  Train braking system model

    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

    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

    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 $)

    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 $)

    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

    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 $)

    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 $)

    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

    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

    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T0.2 s
    制动模型时延Td1.0 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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