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定形态隧道压力波激扰下车内压力迭代学习控制

陈春俊 曹宇啸 何智颖 杨露

陈春俊, 曹宇啸, 何智颖, 杨露. 定形态隧道压力波激扰下车内压力迭代学习控制[J]. 西南交通大学学报, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026
引用本文: 陈春俊, 曹宇啸, 何智颖, 杨露. 定形态隧道压力波激扰下车内压力迭代学习控制[J]. 西南交通大学学报, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026
CHEN Chunjun, CAO Yuxiao, HE Zhiying, YANG Lu. Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026
Citation: CHEN Chunjun, CAO Yuxiao, HE Zhiying, YANG Lu. Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 20-28. doi: 10.3969/j.issn.0258-2724.20211026

定形态隧道压力波激扰下车内压力迭代学习控制

doi: 10.3969/j.issn.0258-2724.20211026
基金项目: 国家自然科学基金(51975487)
详细信息
    作者简介:

    陈春俊(1967—),男,教授,博士生导师,研究方向为轨道交通设备性能测试、诊断与控制,E-mail:cjchen@swjtu.edu.cn

  • 中图分类号: TP273;U271.91

Iterative Learning Control of Interior Pressure Under Excitation of Tunnel Pressure Wave with Fixed Form

  • 摘要:

    同一列车重复通过同一隧道时所产生隧道压力波激扰具有形态相似、变尺度变幅值的特性. 针对现有控制策略未考虑这一定形态特性的问题,提出一种基于高阶反馈遗忘迭代学习的控制方法. 首先,建立高速列车车内外气压传递数学模型,并利用实测车内外压力数据进行修正与验证;其次,通过控制列车通风设备的阀门来减缓车内压力变化,提出阶反馈遗忘迭代学习控制算法,并设计变幅值和变尺度处理方法;最后,利用实测压力波生成一组定形态的随机压力波,并进行仿真分析. 仿真结果表明:在重复定形态的隧道压力波激扰下,高阶反馈遗忘迭代学习控制算法能够使车内压力在第8个迭代周期后1 s变化率基本收敛到200 Pa/s以下,而且均方根误差也在第4个迭代周期后降低到15.0000%以下.

     

  • 图 1  模拟车内压力曲线

    Figure 1.  Simulated curves of pressure inside the train

    图 2  车内压力控制系统框图

    Figure 2.  Block diagram of interior pressure control system

    图 3  定形态的隧道压力波

    Figure 3.  Tunnel pressure waves with a fixed form

    图 4  控制目标的变幅值变换

    Figure 4.  Variable amplitude transformation of control goal

    图 5  控制算法流程

    Figure 5.  Flowchart of control algorithm

    图 6  模拟信号的生成过程

    Figure 6.  Generation process of analog signal

    图 7  2种控制方法的压力1 s变化率范数

    Figure 7.  Norm of pressure change rate within one second for two control methods

    图 8  部分迭代周期的仿真结果

    Figure 8.  Simulation results for part of iteration period

    图 9  3种控制方法的RMSE

    Figure 9.  RMSE of three control methods

    表  1  模拟与实测车内气压的MAPE值

    Table  1.   MAPE values of simulated and measured pressure inside the train

    q1234
    MAPE/%0.370.310.200.25
    下载: 导出CSV

    表  2  参数坐标点

    Table  2.   Coordinates of parameters

    算法峰值点坐标
    高阶遗忘 ILC(2, 18.1035)、(8, 11.3191)、(13, 2.3886)
    传统 P 型 ILC(2, 23.6218)、(8, 20.7152)、(13, 6.2149)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-15
  • 修回日期:  2022-06-02
  • 网络出版日期:  2023-11-15
  • 刊出日期:  2022-06-09

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