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高速动车组数据驱动无模型自适应控制方法

李中奇 周靓 杨辉

李中奇, 周靓, 杨辉. 高速动车组数据驱动无模型自适应控制方法. 自动化学报, 2023, 49(2): 437−447 doi: 10.16383/j.aas.c211068
引用本文: 李中奇, 周靓, 杨辉. 高速动车组数据驱动无模型自适应控制方法. 自动化学报, 2023, 49(2): 437−447 doi: 10.16383/j.aas.c211068
Li Zhong-Qi, Zhou Liang, Yang Hui. Data-driven model-free adaptive control method for high-speed electric multiple unit. Acta Automatica Sinica, 2023, 49(2): 437−447 doi: 10.16383/j.aas.c211068
Citation: Li Zhong-Qi, Zhou Liang, Yang Hui. Data-driven model-free adaptive control method for high-speed electric multiple unit. Acta Automatica Sinica, 2023, 49(2): 437−447 doi: 10.16383/j.aas.c211068

高速动车组数据驱动无模型自适应控制方法

doi: 10.16383/j.aas.c211068
基金项目: 国家自然科学基金(61991404, 52162048, 62003138), 国家重点研发计划重点专项(2020YFB1713703), 江西省主要学科学术和技术带头人培养计划(20213BCJ22002)资助
详细信息
    作者简介:

    李中奇:华东交通大学电气与自动化工程学院教授. 主要研究方向为列车运行过程建模与自适应控制. E-mail: lzq0828@163.com

    周靓:华东交通大学电气与自动化工程学院硕士研究生. 主要研究方向为列车运行过程建模与无模型自适应控制. E-mail: zl971125@163.com

    杨辉:华东交通大学电气与自动化工程学院教授. 主要研究方向为复杂系统建模, 控制与运行优化. 本文通信作者. E-mail: yhshuo@263.com

Data-driven Model-free Adaptive Control Method for High-speed Electric Multiple Unit

Funds: Supported by National Natural Science Foundation of China (61991404, 52162048, 62003138), National Key Research and Development Program of China (2020YFB1713703), and Jiangxi Province Academic and Technical Leaders Training Program (20213BCJ22002)
More Information
    Author Bio:

    LI Zhong-Qi Professor at the Sc-hool of Electrical and Automation Engineering, East China Jiaotong University. His research interest covers modeling the train operation process and adaptive control

    ZHOU Liang Master student at the School of Electrical and Automa-tion Engineering, East China Jiaotong University. His research interest covers modeling the train operation process and model-free adaptive control

    YANG Hui Professor at the Sch-ool of Electrical and Automation Engineering, East China Jiaotong University. His research interest covers modeling complex systems, control, and operation optimization. Corresponding author of this paper

  • 摘要: 针对动车组的速度跟踪控制问题, 同时考虑到现有基于模型的控制方法对系统动力学模型的依赖性, 以及传统无模型自适应控制时变参数估计算法的复杂性, 将改进的多输入多输出(Multiple-input multiple-output, MIMO)偏格式动态线性化无模型自适应控制(Partial form dynamic linearization-improved model-free adaptive control, PFDL-iMFAC)方法引入到动车组自动驾驶系统中. 该控制方法在无模型自适应控制的基础上, 考虑滑动时间窗口, 增加了可调自由度和设计灵活性, 并在输入准则函数中加上对能量函数的惩罚项, 减少能量损耗, 为动车组的跟踪精度和节能运行提供了一种优化的方法, 在满足动车组速度跟踪效果好的前提下实现节能运行. 最后以CRH380A动车组为对象进行仿真实验, 通过与传统无模型自适应控制对比: 所提出的控制算法各动力单元速度跟踪误差在 ±0.2 km/h以内, 加速度在 ±0.65 m/s2以内且变化平稳, 比传统无模型自适应控制方法节约9.86%的能量.
  • 图  1  动车组运行过程动力学描述

    Fig.  1  Dynamic description of electric multiple unit operation process

    图  2  CRH380A型动车组动力单元分布

    Fig.  2  Distribution of CRH380A electric multiple unit power unit

    图  3  改进的动车组无模型自适应控制结构框图

    Fig.  3  An improved block diagram of model-free adaptive control structure for electricmultiple unit

    图  4  本文方法与其他方法速度跟踪曲线对比

    Fig.  4  The velocity tracking curves of the proposed method are compared with those of other methods

    图  5  本文方法与其他方法各动力单元速度跟踪误差对比

    Fig.  5  The velocity tracking errors of the proposed method are compared with those of other methods

    图  6  本文方法与其他方法单位控制力变化对比

    Fig.  6  The variation of unit control force is compared with other methods

    图  7  本文方法与其他方法加速度变化对比

    Fig.  7  The acceleration changes of the proposed method are compared with other methods

    表  1  CRH380A型动车组模型参数

    Table  1  The CRH380A electric multiple unitmodel parameters

    参数名称参数值单位
    动力单元质量$M_1$$ 1.836\times 10^5$kg
    动力单元质量$M_2$$ 1.123\times 10^5 $kg
    动力单元质量$M_3$$ 1.836\times 10^5 $kg
    列车阻力系数$a_r$5.2N/kg
    列车阻力系数$b_r$$ 3.6\times 10^{-2} $${\rm{N} } \cdot {\rm{s} }^2/({\rm{kg} } \cdot {\rm{m} })$
    列车阻力系数$c_r$$ 1.2\times 10^{-3} $${\rm{N} } \cdot {\rm{s} }^2/({\rm{kg} } \cdot {\rm{m}^2 })$
    车钩弹性系数$k$$ 2\times 10^7 $N/m
    车钩阻尼系数$d$$ 5\times 10^6 $${\rm{N}} \cdot {\rm{s/m} }$
    下载: 导出CSV

    表  2  各个控制方法的若干性能指标对比

    Table  2  Comparison of several performance indexes of each control method

    控制方法均方误差最大加减速度 (m/s2)能量损耗节约率 (%)
    文献[19]$1.2\times 10^{-2} $1.0848$ 2.41\times 10^6 $
    PFDL-MFAC$6.2\times 10^{-3} $0.7309$2.29\times 10^6 $$5.04$
    PFDL-iMFAC$6.6\times 10^{-3} $0.6572$2.17\times 10^6$$9.86 $
    下载: 导出CSV
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    [31] 基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案. 控制理论与应用, to be published

    Model-free adaptive control with disturbance rejection based on modified Kalman filter. Control Theory & Applications, to be published
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  • 收稿日期:  2021-11-10
  • 录用日期:  2022-04-28
  • 网络出版日期:  2023-01-11
  • 刊出日期:  2023-02-20

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