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基于制动特征自学习的磁浮列车强化学习制动控制

刘鸿恩 胡闽胜 胡海林

刘鸿恩, 胡闽胜, 胡海林. 基于制动特征自学习的磁浮列车强化学习制动控制[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230517
引用本文: 刘鸿恩, 胡闽胜, 胡海林. 基于制动特征自学习的磁浮列车强化学习制动控制[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230517
LIU Hongen, HU Minsheng, HU Hailin. Reinforcement Learning Braking Control of Maglev Trains Based on Self-Learning of Hybrid Braking Features[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230517
Citation: LIU Hongen, HU Minsheng, HU Hailin. Reinforcement Learning Braking Control of Maglev Trains Based on Self-Learning of Hybrid Braking Features[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230517

基于制动特征自学习的磁浮列车强化学习制动控制

doi: 10.3969/j.issn.0258-2724.20230517
基金项目: 国家自然科学基金(52262050);江西省自然科学基金(20224BAB202025)
详细信息
    作者简介:

    刘鸿恩(1987—),男,讲师,博士,研究方向为轨道交通系统建模与优化控制, E-mail:len3610@126.com

    通讯作者:

    胡海林(1984—),男,副教授,博士,研究方向为磁浮列车牵引驱动系统控制策略, E-mail:huhailin@jxust.Edu.cn

  • 中图分类号: U283.1

Reinforcement Learning Braking Control of Maglev Trains Based on Self-Learning of Hybrid Braking Features

  • 摘要:

    精准、平稳停车是磁浮列车自动驾驶制动控制的重要目标. 中低速磁浮列车停站制动过程受到电-液混合制动状态强耦合等影响,鉴于制动特征理论模型的传统制动控制方法难以保障磁浮列车的停车精度和舒适性. 本文提出一种基于混合制动特征自学习的磁浮列车强化学习制动控制方法. 首先,采用长短期记忆网络建立磁浮列车混合制动特征模型,结合磁浮列车运行环境和状态数据进行动态制动特征自学习;然后,根据动态特征学习结果更新强化学习的奖励函数与学习策略,提出基于深度强化学习的列车制动优化控制方法;最后,采用中低速磁浮列车现场运行数据开展仿真实验. 实验结果表明:本文所提出的制动控制方法较传统方法的舒适性和停车精度分别提高41.18%和22%,证明了本文建模与制动优化控制方法的有效性.

     

  • 图 1  中低速磁浮列车电-液混合制动控制原理

    Figure 1.  Principle of electro-hydraulic hybrid braking control of medium and low-speed maglev trains

    图 2  磁浮列车混合制动动态特性

    Figure 2.  Dynamic features of hybrid braking of maglev train

    图 3  基于LSTM的磁浮列车制动模型

    Figure 3.  Braking model of maglev train based on LSTM

    图 4  BFS-DQN框图

    Figure 4.  Framework of BFS-DQN

    图 5  强化学习制动优化控制算法流程

    Figure 5.  Flow chart of optimization control algorithm for reinforcement learning braking

    图 6  奖励函数变化曲线

    Figure 6.  Change curves of reward function

    图 7  加速度收敛情况

    Figure 7.  Acceleration convergence condition

    图 8  停车误差收敛情况(DQN和BFS-DQN分别取停车误差的绝对值和绝对值负值)

    Figure 8.  Convergence of parking errors (DQN and BFS-DQN take the absolute value and negative value of absolute value of parking error, respectively)

    图 9  制动曲线对比图

    Figure 9.  Braking curve comparison

    图 10  制动等级和加速度变化情况

    Figure 10.  Braking rating and acceleration changes

    图 11  停车误差对比

    Figure 11.  Comparison of parking errors

    表  1  仿真列车参数

    Table  1.   Simulation train parameters

    参数类别 参数特性
    列车质量/t 75
    线路最高限速/(km·h−1 80
    编组数量 3
    最大常用制动力/kN 74.23
    最大常用减速度/(m·s−2 0.96
    线路最大坡度/‰ 51.01
    下载: 导出CSV

    表  2  算法主要训练参数

    Table  2.   Algorithm training parameters

    参数 BFS-DQN DQN
    LSTM 迭代次数/次 500
    LSTM 学习率 0.001
    LSTM 样本批量 50
    单次训练最大步数 80 80
    训练最大次数/次 20000 20000
    Q 网络学习率 0.001 0.001
    Q 网络更新频率 100 100
    样本大小 32 32
    经验池容量 2000 2000
    折扣因子 0.96 0.96
    贪婪率初始值 0.9 0.9
    贪婪率最终值 0.1 0.1
    下载: 导出CSV

    表  3  算法训练结果

    Table  3.   Algorithm training results

    训练结果 BFS-DQN DQN
    平均奖励值 33.5 27.8
    平均状态转移次数/次 70 72
    平均停车误差/m 0.10 0.15
    平均加速度变化/(cm·s−3 10.84 11.78
    平均制动时间/s 14.0 14.4
    下载: 导出CSV

    表  4  算法性能

    Table  4.   Algorithm performance

    制动控制策略 RMSE SD
    BFS-DQN 0.099048 0.070652
    DQN 0.142815 0.110446
    传统 ATO 0.276103 0.140018
    下载: 导出CSV

    表  5  停车误差分布情况

    Table  5.   Distribution of parking errors

    停车误差/m BFS-DQN DQN ATO
    $ x \lt - 0.5 $ 0 0 3
    $ - 0.5 \leqslant x \leqslant - 0.3 $ 0 2 3
    $ - 0.3 \lt x \leqslant 0 $ 18 19 16
    $0 \lt x \leqslant 0.3$ 32 29 20
    $ 0.3 \lt x \leqslant 0.5 $ 0 0 6
    $ x \gt 0.5 $ 0 0 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-09
  • 修回日期:  2024-02-16
  • 网络出版日期:  2024-04-20

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