Volume 23 Issue 2
Apr.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(2): 251-263
LI Zhong-qi, HUANG Lin-jing, ZHOU Liang, YANG Hui, TANG Bo-wei. Sliding mode active disturbance rejection adhesion control method of high-speed train[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 251-263. doi: 10.19818/j.cnki.1671-1637.2023.02.018
Citation: LI Zhong-qi, HUANG Lin-jing, ZHOU Liang, YANG Hui, TANG Bo-wei. Sliding mode active disturbance rejection adhesion control method of high-speed train[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 251-263. doi: 10.19818/j.cnki.1671-1637.2023.02.018
shu

Sliding mode active disturbance rejection adhesion control method of high-speed train

doi: 10.19818/j.cnki.1671-1637.2023.02.018
  • 1.

    School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

  • 2.

    State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Funds:

National Key Research and Development Program of China 2020YFB1713703

National Natural Science Foundation of China 52162048

National Natural Science Foundation of China 61991404

National Natural Science Foundation of China U2034211

Jiangxi Provincial Program for Academic and Technical Leaders Training of Major Disciplines 20213BCJ22002

More Information
  • Author Bio:

    LI Zhong-qi(1975-), male, professor, PhD, lzq0828@163.com

    ZHOU Liang(1997-), male, doctoral student, zl971125@163.com.

  • Received Date: 2022-10-21
  • Publish Date: 2023-04-25
    Abstract
  • In order to solve the problems of idling or sliding due to the change of rail surface during the operation of high-speed train so that train did not reach the maximum adhesive utilization, a sliding mode active disturbance rejection controller (SM-ADRC) of adhesion based on the maximum adhesion coefficient was designed. Considering the complex, time-varying and nonlinear characteristics of wheel-rail adhesion, a mechanical model of wheel-rail traction system was established based on the analysis of adhesion mechanism. The maximum likelihood estimation (MLE) method was used to identify the relevant parameters of different rail surfaces, and the maximum adhesion coefficient of the current rail surface was calculated to ensure that the train could always achieve the maximum adhesion utilization. The nonlinear error feedback control law in the active disturbance rejection control (ADRC) was improved by introducing the sliding mode algorithm, a SM-ADRC algorithm of adhesion was designed, the Levant tracking differentiator was used to reduce the initial tracking error, and the extended state observer (ESO) was used to estimate and compensate the total external disturbance of the system. The robustness of the system was improved by the sliding mode control. The CRH380A high-speed train was simulated by the MATLAB software. When the rail surface condition changed, the SM-ADRC of adhesion controlled the train to track the set speed, and was compared with the proportional-integral-differential (PID) controller, sliding mode controller and ADRC in the simulation results. Simulation results show that the maximum adhesion coefficient of the dry rail surface is 0.160, and the true value is identified at 16 s. The maximum adhesion coefficient of the wet rail surface is 0.106, and the true value is identified at 18 s. The speed tracking error range of the ADRC is within ±1 km·h-1, and the speed tracking error fluctuates greatly after the rail surface changes. The speed tracking error range of the SM-ADRC of adhesion is within ±0.4 km·h-1. After the rail surface changes, the speed tracking error fluctuates less, and the speed is more smooth and stable. The speed control tracking accuracy is higher than PID and sliding mode control methods. It can be seen that the proposed SM-ADRC of adhesion can realize the fast adhesion control of the train and achieve the maximum adhesion utilization.

     

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    • References(44)
  • [1]
    庞红燕. 基于蠕滑加速度的高速列车黏着控制研究[D]. 北京: 北京交通大学, 2014.

    PANG Hong-yan. Study on adhesion control of high-speed train based on slip acceleration[D]. Beijing: Beijing Jiaotong University, 2014. (in Chinese)
    [2]
    胡亮, 杨中平, 林飞. 高速列车牵引传动优化黏着控制方法研究[J]. 电气传动, 2015, 45(3): 53-57. https://www.cnki.com.cn/Article/CJFDTOTAL-DQCZ201503014.htm

    HU Liang, YANG Zhong-ping, LIN Fei. Research of optimal adhesion control method for high-speed train traction[J]. Electric Drive, 2015, 45(3): 53-57. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DQCZ201503014.htm
    [3]
    王颖超. 高速动车组粘着控制算法研究[D]. 北京: 北京交通大学, 2009.

    WANG Ying-chao. Study of the adhesion control arithmetic of China high speed EMU[D]. Beijing: Beijing Jiaotong University, 2009. (in Chinese)
    [4]
    左新甜. 基于最优蠕滑率的重载机车防空转控制[D]. 株洲: 湖南工业大学, 2019.

    ZUO Xin-tian. Anti-slip control of heavy-haul locomotive based on optimal creep ratio[D]. Zhuzhou: Hunan University of Technology, 2019. (in Chinese)
    [5]
    何静, 刘建华, 张昌凡. 重载机车轮轨黏着利用技术研究综述[J]. 铁道学报, 2018, 40(9): 30-39. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201809006.htm

    HE Jing, LIU Jian-hua, ZHANG Chang-fan. An overview on wheel-rail adhesion utilization of heavy-haul locomotive[J]. Journal of the China Railway Society, 2018, 40(9): 30-39. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201809006.htm
    [6]
    林文立, 刘志刚, 方攸同. 地铁列车牵引传动再粘着优化控制策略[J]. 西南交通大学学报, 2012, 47(3): 465-470. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201203017.htm

    LIN Wen-li, LIU Zhi-gang, FANG You-tong. Re-adhesion optimization control strategy for metro traction[J]. Journal of Southwest Jiaotong University, 2012, 47(3): 465-470. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT201203017.htm
    [7]
    LU Kuan, SONG Yong-duan, CAI Wen-chuan. Robust adaptive re-adhesion control for high speed trains[C]//IEEE. 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao: IEEE, 2014: 1215-1220.
    [8]
    魏银花, 田广科, 董海鹰. 基于云模型的高速列车黏着控制[J]. 铁道科学与工程学报, 2019, 16(6): 1391-1397. doi: 10.19713/j.cnki.43-1423/u.2019.06.005

    WEI Yin-hua, TIAN Guang-ke, DONG Hai-ying. Adhesion control of the high speed based on cloud model[J]. Journal of Railway Science and Engineering, 2019, 16 (6): 1391-1397. (in Chinese) doi: 10.19713/j.cnki.43-1423/u.2019.06.005
    [9]
    张佳波, 马法运, 刘天宇, 等. 基于组合校正的城市轨道交通列车轮轨黏着控制方法研究[J]. 城市轨道交通研究, 2020, 23(3): 140-143, 147. https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT202003035.htm

    ZHANG Jia-bo, MA Fa-yun, LIU Tian-yu, et al. Wheel/rail adhesion control of urban rail transit vehicle based on combined correction method[J]. Urban Mass Transit, 2020, 23(3): 140-143, 147. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT202003035.htm
    [10]
    GAO Rui-zhen, WANG Yu-juan, LAI Jun-feng, et al. Neuro-adaptive fault-tolerant control of high speed trains under traction- braking failures using self-structuring neural networks[J]. Information Sciences, 2016, 367/368: 449-462. doi: 10.1016/j.ins.2016.05.033
    [11]
    谢国, 金永泽, 黑新宏, 等. 列车动力学模型时变环境参数自适应辨识[J]. 自动化学报, 2019, 45(12): 2268-2280. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201912008.htm

    XIE Guo, JIN Yong-ze, HEI Xin-hong, et al. Adaptive identification of time-varying environmental parameters in train dynamics model[J]. Acta Automatica Sinica, 2019, 45(12): 2268-2280. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201912008.htm
    [12]
    何静, 何云国, 张昌凡, 等. EKF在机车最优黏着控制中的应用[J]. 电子测量与仪器学报, 2019, 33(2): 25-31. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201902003.htm

    HE Jing, HE Yun-guo, ZHANG Chang-fan, et al. Application of EKF in locomotive optimal adhesion control[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(2): 25-31. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201902003.htm
    [13]
    李中奇, 孟凡晖, 杨辉. 基于最优蠕滑率的列车防滑控制研究[J]. 控制工程, 2021, 28(12): 2312-2317. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202112004.htm

    LI Zhong-qi, MENG Fan-hui, YANG Hui. Research on anti-skid control of train based on optimal creep rate[J]. Control Engineering of China, 2021, 28(12): 2312-2317. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202112004.htm
    [14]
    YUAN Lei, ZHAO Hai-yan, CHEN Hong, et al. Nonlinear MPC-based slip control for electric vehicles with vehicle safety constraints[J]. Mechatronics, 2016, 38: 1-15. doi: 10.1016/j.mechatronics.2016.05.006
    [15]
    陈哲明, 曾京, 关庆华. 高速列车再生制动防滑控制及仿真研究[J]. 中国铁道科学, 2010, 31(1): 93-98. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201001019.htm

    CHEN Zhe-ming, ZENG Jing, GUAN Qing-hua. Simulation research on the anti-skid control under the regenerative braking of high-speed train[J]. China Railway Science, 2010, 31(1): 93-98. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201001019.htm
    [16]
    赵凯辉, 李燕飞, 张昌凡, 等. 重载机车滑模极值搜索最优粘着控制研究[J]. 电子测量与仪器学报, 2018, 32(3): 88-95.

    ZHAO Kai-hui, LI Yan-fei, ZHANG Chang-fan, et al. Optimal adhesion control for heavy-haul locomotive based on extremum seeking with sliding mode[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(3): 88-95. (in Chinese)
    [17]
    CHEOK A D, SHIOMI S. Combined heuristic knowledge and limited measurement based fuzzy logic antiskid control for railway applications[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2000, 30(4): 557-568.
    [18]
    李宁洲, 冯晓云. 基于自适应子群协作QPSO算法的机车黏着智能模糊优化控制[J]. 中国铁道科学, 2014, 35(4): 100-107. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201404016.htm

    LI Ning-zhou, FENG Xiao-yun. Intelligent fuzzy optimal control of locomotive adhesion based on adaptive multiple subgroup collaboration QPSO algorithm[J]. China Railway Science, 2014, 35(4): 100-107. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201404016.htm
    [19]
    姚远, 张红军, 罗赟, 等. 基于虚拟样机的机车黏着控制研究[J]. 铁道学报, 2010, 32(6): 96-100. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201006023.htm

    YAO Yuan, ZHANG Hong-jun, LUO Yun, et al. Adhesion control of locomotive based on virtual prototype[J]. Journal of the China Railway Society, 2010, 32(6): 96-100. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201006023.htm
    [20]
    CASTILLO J J, CABRERA J A, GUERRA A J, et al. A novel electrohydraulic brake system with tire-road friction estimation and continuous brake pressure control[J]. IEEE Transactions on Industrial Electronics, 2016, 63(3): 1863-1875.
    [21]
    ZHOU Mei-mei, SONG Yong-duan, CAI Wen-chuan, et al. Neuro-adaptive anti-slip brake control of high-speed trains[C]// IEEE. Proceedings of the 32nd Chinese Control Conference. New York: IEEE, 2013: 291-296.
    [22]
    戚壮, 李芾, 丁军君. 货车极限黏着制动优化方法[J]. 交通运输工程学报, 2012, 12(6): 35-40, 54. doi: 10.19818/j.cnki.1671-1637.2012.06.006

    QI Zhuang, LI Fu, DING Jun-jun. Braking optimization method of wagon under limit adhesion[J]. Journal of Traffic and Transportation Engineering, 2012, 12(6): 35-40, 54. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2012.06.006
    [23]
    UYULAN C, GOKASAN M, BOGOSYAN S. Re-adhesion control strategy based on the optimal slip velocity seeking method[J]. Journal of Modern Transportation, 2018, 26(1): 36-48.
    [24]
    徐传芳, 陈希有, 郑祥, 等. 基于动态面方法的高速列车蠕滑速度跟踪控制[J]. 铁道学报, 2020, 42(2): 41-49. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202002006.htm

    XU Chuan-fang, CHEN Xi-you, ZHENG Xiang, et al. Slip velocity tracking control of high-speed train using dynamic surface method[J]. Journal of the China Railway Society, 2020, 42(2): 41-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202002006.htm
    [25]
    连文博, 刘伯鸿, 李婉婉, 等. 基于自抗扰控制的高速列车自动驾驶速度控制[J]. 铁道学报, 2020, 42(1): 76-81. https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202001013.htm

    LIAN Wen-bo, LIU Bo-hong, LI Wan-wan, et al. Automatic operation speed control of high-speed train based on ADRC[J]. Journal of the China Railway Society, 2020, 42(1): 76-81. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB202001013.htm
    [26]
    李中奇, 金柏, 杨辉, 等. 高速动车组强耦合模型的分布式滑模控制策略[J]. 自动化学报, 2020, 46(3): 495-508. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO202003009.htm

    LI Zhong-qi, JIN Bai, YANG Hui, et al. Distributed sliding mode control strategy for high-speed EMU strong coupling model[J]. Acta Automatica Sinica, 2020, 46(3): 495-508. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO202003009.htm
    [27]
    吴艳, 王丽芳, 李芳. 基于滑模自抗扰的智能车路径跟踪控制[J]. 控制与决策, 2019, 34(10): 2150-2156. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201910012.htm

    WU Yan, WANG Li-fang, LI Fang. Intelligent vehicle path following control based on sliding mode active disturbance rejection control[J]. Control and Decision, 2019, 34(10): 2150-2156. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201910012.htm
    [28]
    刘国福. 基于滑移率的车辆防抱死制动系统的研究[D]. 长沙: 国防科学技术大学, 2007.

    LIU Guo-fu. An investigation of vehicle anti-lock braking system based on slip-ratio[D]. Changsha: National University of Defense Technology, 2007. (in Chinese)
    [29]
    王立玲, 董力元, 马东, 等. 滑动与打滑条件下的轮式移动机器人自抗扰跟踪控制[J]. 控制理论与应用, 2020, 37(2): 431-438. https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY202002021.htm

    WANG Li-ling, DONG Li-yuan, MA Dong, et al. Active disturbance rejection tracking control of wheeled mobile robots under sliding and slipping conditions[J]. Control Theory and Applications, 2020, 37(2): 431-438. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY202002021.htm
    [30]
    薛晗, 邵哲平, 方琼林, 等. 具有输入时滞的二轮自平衡车自适应滑模控制[J]. 交通运输工程学报, 2020, 20(2): 219-228. doi: 10.19818/j.cnki.1671-1637.2020.02.018

    XUE Han, SHAO Zhe-ping, FANG Qiong-lin, et al. Adaptive sliding mode control for two- wheeled self- balancing vehicle with input delay[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 219-228. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.018
    [31]
    范柏旺. 基于模型预测和自抗扰控制的多工况优化的自适应巡航系统研究[D]. 济南: 山东大学, 2020.

    FAN Bai-wang. Research on multi-condition optimization of adaptive cruise control system based on MPC and ADRC[D]. Jinan: Shandong University, 2020. (in Chinese)
    [32]
    何之煜, 杨志杰, 吕旌阳. 基于自适应模糊滑模的列车精确停车制动控制算法[J]. 中国铁道科学, 2019, 40(2): 122-129. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201902017.htm

    HE Zhi-yu, YANG Zhi-jie, LYU Jing-yang. Braking control algorithm for accurate train stopping based on adaptive fuzzy sliding mode[J]. China Railway Science, 2019, 40(2): 122-129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201902017.htm
    [33]
    朱文良, 吴萌岭, 田春, 等. 基于多学科协同分析的轨道车辆制动系统集成化仿真平台[J]. 交通运输工程学报, 2017, 17(3): 99-110. http://transport.chd.edu.cn/article/id/201703011

    ZHU Wen-liang, WU Meng-ling, TIAN Chun, et al. Integrated simulation platform of braking system of rolling stock based on multi-discipline collaborative analysis[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 99-110. (in Chinese) http://transport.chd.edu.cn/article/id/201703011
    [34]
    ZHU Li, HE Ying, YU F R, et al. Communication-based train control system performance optimization using deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 10705-10717.
    [35]
    CHEN De-wang, CHEN Rong, LI Yi-dong, et al. Online learning algorithms for train automatic stop control using precise location data of balises[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1526-1535.
    [36]
    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[J]. 自动化学报, 2023, 49(2): 437-447.

    LI Zhong-qi, ZHOU Liang, YANG Hui. Data-driven model-free adaptive control method for high-speed electric multiple unit[J]. Acta Automatica Sinica, 2023, 49(2): 437-447. (in Chinese)
    [37]
    LI Wei, XIAN Kai, YIN Jia-teng, et al. Developing train station parking algorithms: new frameworks based on fuzzy reinforcement learning[J]. Journal of Advanced Transportation, 2019, 2019: 1-9.
    [38]
    ZIREK A, ONAT A. A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence[J]. Railway Engineering Science, 2020, 28(4): 346-364.
    [39]
    柳海科. 基于粘滑特性的高速列车最优粘着控制研究[D]. 兰州: 兰州交通大学, 2020.

    LIU Hai-ke. Study on optimal adhesion control of high-speed train based on adhesion slip characteristics[D]. Lanzhou: Lanzhou Jiaotong University, 2020. (in Chinese)
    [40]
    ZHOU Liang, LI Zhong-qi, YANG Hui, et al. Data-driven model-free adaptive sliding mode control based on FFDL for electric multiple units[J]. Applied Sciences, 2022, 12(21): 10983.
    [41]
    何云国. 高速列车黏着集成防滑控制方法[D]. 株洲: 湖南工业大学, 2019.

    HE Yun-guo. High speed trains adhesion integrated anti-skid control method[D]. Zhuzhou: Hunan University of Technology, 2019. (in Chinese)
    [42]
    吴业庆, 赵旭峰, 喻励志, 等. 基于最优蠕滑辨识的高速列车黏着控制研究[J]. 机车电传动, 2020(2): 12-16. https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC202002003.htm

    WU Ye-qing, ZHAO Xu-feng, YU Li-zhi, et al. Research on adhesion control based on optimal creep identification of high-speed train[J]. Electric Drive for Locomotives, 2020(2): 12-16. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC202002003.htm
    [43]
    李云峰. 基于最优蠕滑率的粘着控制方法研究[D]. 成都: 西南交通大学, 2011.

    LI Yun-feng. Research on the adhesion control methods based on the optimal creep rate[D]. Chengdu: Southwest Jiaotong University, 2011. (in Chinese)
    [44]
    YIN Jia-teng, CHEN De-wang, LI Ling-xi. Intelligent train operation algorithms for subway by expert system and reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(6): 2561-2571.
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