Distributed Sliding Mode Control Strategy for High-speed EMU Strong Coupling Model

被引：0
|
作者：
Li Z.-Q. [1 ,2 ]
Jin B. [1 ,2 ]
Yang H. [1 ,2 ]
Tan C. [1 ,2 ]
Fu Y.-T. [1 ,2 ]
机构：
[1] School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang
[2] Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Data compensation; Distributed neural network sliding mode control; High-speed eletric multiple units (EMU); Strong coupling model; Tracking control;
D O I：
10.16383/j.aas.190216
中图分类号：
学科分类号：
摘要：
The high-speed EMU (electric multiple units) is a complex system composed of multi-section vehicles and hooking devices. This paper compares the hooking device into a spring-damper system, and analyzes the dynamic mechanism and mode of the action of coupler and buffer device on adjacent vehicles during the operation of high-speed EMU, and then, establishes the strong coupling model of high-speed EMU. According to the decentralized characteristics of the input of train model power or braking force, a distributed neural network sliding mode control strategy is designed to track the speed of high-speed EMU. In order to reduce the influence of unknown factors on the control accuracy of high-speed EMU during speed tracking, using the historical train operating data, the historical operating data center is used to compensate the current control law to improve control accuracy and practical stability. The simulation results of the high-speed EMU Operation Simulation Platform show that the modeling method can better reflect the operation characteristics of the high-speed EMU than the previous multi-point model, and the control strategy with compensation rules is better than the traditional control effect. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：495 / 508
页数：13
相关论文
共 34 条
  • [1] Dong H R, Ning B, Cai B, Hou Z S., Automatic train control system development and simulation for high-speed railways, IEEE Circuits and Systems Magazine, 10, 2, pp. 6-18, (2010)
  • [2] Chen C L., Reshaping Chinese space-economy through high-speed trains: opportunities and challenges, Journal of Transport Geography, 22, 2, pp. 312-316, (2012)
  • [3] Wu Meng-Ling, Cheng Guang-Hua, Wang Xiao-Yan, Jin Bi-Yun, Discussion on the problem of train brake deceleration control, Journal of the China Railway Society, 31, 1, pp. 94-97, (2009)
  • [4] Li De-Cang, Meng Jian-Jun, Xu Ru-Xun, Yin Ming, Adaptive robust H<sub>∞</sub> control method for high speed trains under high winds, Journal of the China Railway Society, 40, 7, pp. 67-73, (2018)
  • [5] Zhang Shou-Shuai, Tian Chang-Hai, Study on the related problems of train running speed in long and downhill sections of high speed railway, China Railway Science, 38, 3, pp. 124-129, (2017)
  • [6] Lu Yu-Jiang, Xiao Shou-Na, Zhu Tao, Yang Guang-Wu, Yang Chao, Modeling and research on coupled longitudinal-vertical collision dynamics model, Journal of the China Railway Society, 36, 12, pp. 6-13, (2014)
  • [7] Shabana A., Ding Li-Fen, Xie Ji-Long, Ahmed A, Application of non-inertial coordinates in longitudinal force analysis of trains, Journal of the China Railway Society, 34, 1, pp. 13-18, (2012)
  • [8] Li Zhong-Qi, Yang Hui, Liu Ming-Jie, Liu Jie-Min, Modeling and tracking control of high speed EMU braking process, China Railway Science, 37, 5, pp. 80-86, (2016)
  • [9] Yang Hui, Zhang Kun-Peng, Wang Xin, Zhong Lu-Sheng, Multi-model generalized predictive control method for high-speed trains, Journal of the China Railway Society, 33, 8, pp. 80-87, (2011)
  • [10] Yu Jin, He Zheng-You, Qian Qing-Quan, Research on multi-objective train control based on hybrid particle swarm optimization, Journal of the China Railway Society, 32, 1, pp. 38-42, (2010)
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