Volume 23 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
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

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

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

     

  • loading
  • [1]
    PANG Hong-yan. Study on adhesion control of high-speed train based on slip acceleration[D]. Beijing: Beijing Jiaotong University, 2014. (in Chinese)
    [2]
    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]
    WANG Ying-chao. Study of the adhesion control arithmetic of China high speed EMU[D]. Beijing: Beijing Jiaotong University, 2009. (in Chinese)
    [4]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    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]
    HE Yun-guo. High speed trains adhesion integrated anti-skid control method[D]. Zhuzhou: Hunan University of Technology, 2019. (in Chinese)
    [42]
    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]
    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.

Catalog

    Article Metrics

    Article views (615) PDF downloads(73) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return