Sliding Mode Control of Maglev Train Suspension System with Neural Network Acceleration Feedback

被引：0
|
作者：
Chen C. [1 ,2 ,3 ]
Xu J. [2 ]
Lin G. [2 ]
Rong L. [2 ]
Sun Y. [2 ]
机构：
[1] Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai
[2] National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai
[3] College of Transportation Engineering, Tongji University, Shanghai
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2021年 / 49卷 / 12期
关键词：
Acceleration feedback; Maglev train; Nonlinear model of levitation system; Radial basis function (RBF) compensation control; Sliding mode control;
D O I：
10.11908/j.issn.0253-374x.21206
中图分类号：
学科分类号：
摘要：
In order to ensure the suspension stability of maglev train, the active control of suspension system is studied. Firstly, based on the minimum suspension unit of single electromagnet of maglev train, the corresponding control mathematical model of current is established. Combined with the simulation, it is shown that the proportion-integration-differentiation(PID)control algorithm is very sensitive to time-varying disturbances such as nonlinear load. Then, a sliding mode control method based on the stability proof of bifurcation theory is proposed. Combined with the parameter self-adjusting function of radial basis function (RBF) neural network, a suspension control module with vibration suppression is constructed to effectively suppress the vibration of electromagnet. Finally, the Simulink control model is constructed and the single electromagnet suspension experimental platform is built for relevant simulation and experiments. The results show that the effect of electromagnet vibration on the suspension performance is particularly obvious. The proposed control algorithm can effectively suppress the electromagnet vibration in the presence of complex disturbances and improve the dynamic performance of the suspension system. © 2021, Editorial Department of Journal of Tongji University. All right reserved.
引用
收藏
页码：1642 / 1651
页数：9
相关论文
共 17 条
  • [1] WU Xiangming, Maglev train, (2003)
  • [2] LEE H W, KIM K C, LEE J., Review of maglev train technologies, IEEE Transactions on Magnetics, 42, 7, (2006)
  • [3] XU Fei, LUO Shihui, DENG Zigang, Study on key technologies and whole speed range application of maglev rail transport, Journal of China Railway Society, 41, 3, (2019)
  • [4] LONG Zhiqiang, HAO Aming, CHANG Wensen, Suspension controller design of maglev train considering the rail track periodical irregularity, Journal of National University of Defense Technology, 25, 2, (2003)
  • [5] ZHANG Wenyue, TONG Laisheng, ZHU Yue'ou, Et al., Back-stepping control and experimental study of maglev train suspension system, Science Technology and Engineering, 21, 4, (2021)
  • [6] AO Jian'an, WEI Gaoheng, MA Weihua, Et al., Influence of geometry accuracy of track structure on dynamic performance of the coupled medium-low speed maglev train-rail-bridge system, Journal of Railway Science and Engineering, 17, 11, (2020)
  • [7] KONG E, SONG J S, KANG B B, Et al., Dynamic response and robust control of coupled maglev vehicle and guideway system, Journal of Sound and Vibration, 330, 25, (2011)
  • [8] WAI R J, LEE J D., Robust levitation control for linear maglev rail system using fuzzy neural network, IEEE Transactions on Control System Technology, 17, 1, (2009)
  • [9] WAI R J, LEE J D, CHUANG K L., Real-time PID control strategy for maglev transportation system via particle swarm optimization, IEEE Transactions on Industrial Electrionics, 58, 2, (2011)
  • [10] LIU Lili, ZUO Jihong, Parameter self-adjusting control method of fuzzy PID for magnetic levitation ball system, Control Engineering of China, 28, 2, (2021)
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