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 |
[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.
|