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基于钩缓约束的重载列车驾驶过程优化

付雅婷 原俊荣 李中奇 杨辉

付雅婷, 原俊荣, 李中奇, 杨辉. 基于钩缓约束的重载列车驾驶过程优化. 自动化学报, 2019, 45(12): 2355−2365 doi: 10.16383/j.aas.c190223
引用本文: 付雅婷, 原俊荣, 李中奇, 杨辉. 基于钩缓约束的重载列车驾驶过程优化. 自动化学报, 2019, 45(12): 2355−2365 doi: 10.16383/j.aas.c190223
Fu Ya-Ting, Yuan Jun-Rong, Li Zhong-Qi, Yang Hui. Optimization of heavy haul train operation process based on coupler constraints. Acta Automatica Sinica, 2019, 45(12): 2355−2365 doi: 10.16383/j.aas.c190223
Citation: Fu Ya-Ting, Yuan Jun-Rong, Li Zhong-Qi, Yang Hui. Optimization of heavy haul train operation process based on coupler constraints. Acta Automatica Sinica, 2019, 45(12): 2355−2365 doi: 10.16383/j.aas.c190223

基于钩缓约束的重载列车驾驶过程优化

doi: 10.16383/j.aas.c190223
基金项目: 国家自然科学基金(61673172, 51565012, 61733005, 61803155, 61663013)资助
详细信息
    作者简介:

    付雅婷:博士, 华东交通大学电气与自动化工程学院讲师. 主要研究方向为轨道交通运行优化控制. E-mail: fuyating0103@163.com

    原俊荣:华东交通大学电气与自动化工程学院硕士研究生. 主要研究方向为重载列车运行优化控制. E-mail: gfnjl@163.com

    李中奇:博士, 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控制. E-mail: lzq0828@163.com

    杨辉:博士, 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.net

Optimization of Heavy Haul Train Operation Process Based on Coupler Constraints

Funds: Supported by National Natural Science Foundation of China (61673172, 51565012, 61733005, 61803155, 61663013)
  • 摘要: 重载列车是一种由上百甚至几百节车厢组成的动力集中式大载重系统, 其牵引力/制动力需通过车钩相继传递给车厢, 存在明显的非线性和大滞后性. 现有的人工驾驶模式, 司机难以考虑车厢之间的钩缓约束, 易引起车钩断裂和脱轨; 且运行性能与司机的操纵经验密切相关, 存在耗电大, 无法按照列车运行图正点运行等问题. 本文针对此关键问题, 以实现重载列车安全、正点、节能运行为目标, 开展其驾驶过程运行优化研究. 分析列车钩缓系统受力原理, 基于其特性曲线, 采用翟方法构造重载列车钩缓模型及整车纵向动力学模型; 据此, 考虑钩缓约束运用多目标自适应遗传算法, 结合实际运行线路(限速、坡道、曲线率等)约束条件设定列车理想的运行速度目标曲线; 最后, 采用改进广义预测控制器设计重载列车驾驶过程优化控制方法, 跟踪理想速度目标曲线安全、正点、低能耗运行. 基于大秦线上HXD1型重载列车实际数据的仿真结果表明本文所设计的理想目标速度曲线优化方法可以较好地改善列车运行中的安全, 正点和节能等关键性指标, 运行优化控制能保证列车精确跟踪理想速度目标曲线, 实现其驾驶过程优化运行.
    1)  1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013    2. Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 330013
    2)  收稿日期 2019-03-22    录用日期 2019-06-02 Manuscript received March 22, 2019; accepted June 2, 2019 国家自然科学基金 (61673172, 51565012, 61733005, 61803155, 61663013) 资助 Supported by National Natural Science Foundation of China (61673172, 51565012, 61733005, 61803155, 61663013) 本文责任编委 董海荣 Recommended by Associate Editor DONG Hai-Rong 1. 华东交通大学电气与自动化工程学院 南昌 330013    2. 江西省先进控制与优化重点实验室 南昌 330013
  • 图  1  重载列车纵向动力学模型

    Fig.  1  Longitudinal dynamic model of heavy haul train

    图  2  弹性胶泥缓冲器特性曲线

    Fig.  2  Elastic clay buffer characteristic curves

    图  3  重载列车多质点模型运行计算流程图

    Fig.  3  Flow charts of multi-particle model operation calculation for heavy haul train

    图  4  多目标自适应遗传算法计算流程图

    Fig.  4  Computational flow chart of multi-objective adaptive genetic algorithms

    图  5  湖东二场 — 阳原区段部分线路数据

    Fig.  5  Partial line data of Hudongerchang — Yangyuan section

    图  6  文献[24]中9个机构仿真的最大车钩力

    Fig.  6  Maximum coupler force of nine mechanisms simulated in [24]

    图  8  文献[24]中第10号车钩车钩力变化趋势

    Fig.  8  Tendency of coupler force change of coupler No.10 in [24]

    图  9  本文仿真的第10号车钩车钩力变化趋势

    Fig.  9  Tendency of coupler force change of coupler No. 10 simulated in this paper

    图  7  本文仿真的最大车钩力

    Fig.  7  Maximum coupler force simulated in this paper

    图  10  重载列车理想运行目标曲线

    Fig.  10  Ideal train operation curve of heavy haul train

    图  11  本文方法、文献[14]方法优化后运行与实际驾驶最大车钩力

    Fig.  11  Maximum coupler forces of optimized operation in this paper, [14] and actual operation

    图  12  多目标优化策略遗传算法适应度

    Fig.  12  Multiple target optimal policy genetic algorithm fitness

    图  13  改进广义预测控制速度跟踪曲线

    Fig.  13  Speed tracking of IGPC

    图  14  改进广义预测控制牵引/制动力曲线

    Fig.  14  Control force of IGPC

    表  1  本文方法、文献[14]方法优化后多目标数据与实际驾驶数据对比

    Table  1  Data comparison among multiple target optimal policy in this paper, [14] and actual operation

    时间 (s)能耗 (kW)安全系数最大拉钩 (kN)最大压钩 (kN)
    本文3 383.13 505.3−821.91 160.7
    司机驾驶3 5104 200−1 347.71 787.6
    文献 [14]3 3793 929−1 170.32 009
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-22
  • 录用日期:  2019-06-02
  • 网络出版日期:  2019-12-06
  • 刊出日期:  2019-12-01

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