Velocity control algorithm of high-speed trains based on RBF-ADRC

doi:10.16265/j.cnki.issn1003-3033.2022.06.2683

Abstract

Abstract:

Considering time-varying problems and nonlinear model of high-speed trains during operation, an ADRC algorithm for train velocity based on radial basis function (RBF) neural network(RBFNN) optimization was proposed. Firstly, a train dynamics equation was established based on single mass point model. Secondly, ADRC technology was applied to trains. With their external interference as expansion part, ADRC controller based on RBFNN optimization was designed by using nonlinear error feedback control law to observe and compensate system disturbance in real time. Then, target speed curve was simulated and tracked with parameters of crh380 train to verify tracking performance of RBF-ADRC controller. Finally, it was compared with the traditional ADRC controller in tracking accuracy and tracking error. The results show that its tracking accuracy is higher than that of the traditional one, and tracking error is smaller, which is suitable for strict operation conditions of trains.

Key words: high-speed train, radial basis function(RBF) neural network(RBFNN), auto disturbance rejection control(ADRC), target speed curve, tracking performance

SONG Li, GUO Wei, LI Fei, LIU Leyu. Velocity control algorithm of high-speed trains based on RBF-ADRC[J]. China Safety Science Journal, 2022, 32(6): 131-136.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

References 16

[1]	YIN Jie, TANG Ting, YANG Lixin. Research and development of automatic train operation for railway transportation systems: a survey[J]. Transportation Research Part C: Emerging Technologies, 2017, 12 (1): 548-572.
[2]	VAPNIK V N. The nature of statistical learning theory[M]. New York: Springer, 2018.12-37.
[3]	CAO Yong, MA Licong, ZHANG Yaozhong. Application of fuzzy predictive control technology in automatic train operation[J]. Cluster Computing, 2018, 22 (6): 78-85.
[4]	LI Zhixin, HOU Zhisong. Adaptive iterative learning control based high-speed train operation tracking under iteration-varying parameter and measurement noise[J]. Asian Journal of Control, 2015, 17 (5):1-10. doi: 10.1002/asjc.1056
[5]	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.
	LIAN Wenbo, LIU Bohong, LI Wanwan, et al. Automatic driving speed control of high speed train based on active disturbancerejection control[J] Journal of railways, 2020, 42 (1): 76-81.
[6]	QIN Yu, PENG Hao, RUAN Wei. A modeling and control approch to magnetic levitation system based on state-dependent ARX model[J]. Journal of Process Control, 2014, 24(1):93-112. doi: 10.1016/j.jprocont.2013.10.016
[7]	姚晓艳. 电动负载模拟器自抗扰控制器设计方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	YAO Xiaoyan. Research on design method of ADRC for electric load simulator[D] Harbin: Harbin Institute of technology, 2018.
[8]	芮宏斌, 曹伟, 王天赐. 基于改进型自抗扰控制的光伏板清洁机器人路径跟踪控制研究[J]. 机械设计, 2021, 38(2): 79-83.
	RUI Hongbin, CAO Wei, WANG Tianci. Research on path tracking control of photovoltaic panel cleaning robot based onimproved active disturbance rejection control[J]. Mechanical Design, 2021, 38 (2): 79-83.
[9]	高钦和, 董家臣. 扩张状态观测器的观测误差前馈补偿设计[J]. 国防科技大学学报, 2019, 41(5): 93-102.
	GAO Qinhe, DONG Jiachen. Feedforward compensation design of observation error for extended state observer[J]. Journal of National University of Defense Technology, 2019, 41 (5): 93-102.
[10]	GONG Chenglong, JIANG Yuan, LYU Ke. Improved composite nonlinear feedback control for robot manipulators[J]. Journal of Donghua University:English Edition, 2018, 35(6):464-468.
[11]	XIAO Yiting. A model predictive control approch to inverted pendulum system based on RBF-ARX model[C]. Procesdingd of the 37^th Chinese Control Conference, 2018:1-6.
[12]	白杰, 朱日兴, 王伟, 等. 基于线性自抗扰控制技术控制器设计的控制方法[J]. 科学技术与工程, 2020, 20(10):4149-4153.
	BAI Jie, ZHU Rixing, WANG Wei, et al. Control method of controller design based on linear active disturbance rejection control technology[J] Science, Technology and Engineering, 2020, 20 (10): 4149-4153.
[13]	齐金平, 王康, 周亚辉, 等. 基于Markov的列控中心可用度及剩余寿命预测[J]. 中国安全科学学报, 2021, 31(10): 54-61. doi: 10.16265/j.cnki.issn1003-3033.2021.10.008
	QI Jinping, WANG Kang, ZHOU Yahui, et al. Prediction of availability and remaining life of train control center based on Markov[J]. China Safety Science Journal, 2021, 31(10):54-61. doi: 10.16265/j.cnki.issn1003-3033.2021.10.008
[14]	CHEN Lingji, NARENDRA K S. Nonlinear adaptive control using neural networks and multiple models[J]. Automatic, 2020,37: 45-55.
[15]	张攀科, 罗帆. 水上机场航道冲突风险机制的FTA-BN建模[J]. 中国安全科学学报, 2018, 28(9): 177-182. doi: 10.16265/j.cnki.issn1003-3033.2018.09.030
	ZHANG Panke, LUO Fan. FTA-BN modeling of runway conflict risk mechanism at water aerodrome[J]. China Safety Science Journal, 2018, 28(9): 177-182. doi: 10.16265/j.cnki.issn1003-3033.2018.09.030
[16]	丛孟新. 基于自抗扰的高速列车速度控制方案设计[D]. 青岛: 青岛科技大学, 2021.
	CONG Mengxin. Design of high speed train speed control scheme based on active disturbance rejection[D] Qingdao: Qingdao University of Science and Technology, 2021.