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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2017  >  17(1): 71-81
XIE Guo, ZHANG Dan, HEI Xin-hong, QIAN Fu-cai, CAO Yuan, CAI Bo-gen, GAO Qiao-sheng, WANG Yue-kuan. Online identification method of time-varying parameters for longitudinal dynamics model of high-speed train[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 71-81.
Citation: XIE Guo, ZHANG Dan, HEI Xin-hong, QIAN Fu-cai, CAO Yuan, CAI Bo-gen, GAO Qiao-sheng, WANG Yue-kuan. Online identification method of time-varying parameters for longitudinal dynamics model of high-speed train[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 71-81.
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Online identification method of time-varying parameters for longitudinal dynamics model of high-speed train

  • 1.

    Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, Shaanxi, China

  • 2.

    Quanzhou Institute of Equipment Manufacturing, Chinese Academy of Sciences, Quanzhou 362200, Fujian, China

  • 3.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 4.

    Department of Computer Engineering, Nihon University, Funabashi 274-8501, Chiba, Japan

More Information
  • Author Bio:

    XIE Guo(1982-), male, associate professor, PhD, +86-29-82312006, guoxie@xaut.edu.cn

  • Received Date: 2016-10-11
  • Publish Date: 2017-02-25
    Abstract
  • In view of the longitudinal dynamics characteristic of high-speed train, the relationships between traction, braking force, resistance and velocity and acceleration were analyzed.The random disturbance caused by weather and route in high-speed train running condition was studied, the random effect on the structural parameters of train model caused by mechanical wearand running environment was analyzed, and a nonlinear parametric state space model was established to describe the longitudinal dynamics characteristic of high-speed train under the disturbances of noises.The conditional mathematical expectations of the model parameters were calculated by the expectation maximization (EM) algorithm, and the state of the train with particles parameters was estimated by combining the particle filter theory.The online identification method of time-varying parameters for the nonlinear dynamics model of high-speed train was set up based on the Bayesian posterior probability theory, and the real-time state of train was estimated.The parameters of Gaussian and Exponential distribution under Gaussian or Gamma noise were identified by Monte Carlo simulation.The simulation result shows that the relative errors of estimated and real speeds and displacements of high-speed train are less than 5%, and the relative errors of estimated parameters and real parameters of train model are less than 10%, which meets the actual requirement of train system.So, when the probability distributions of time-varying parameters are given, the states and model parameters of train can be identified and estimated under Gaussian or Gamma noise by the method.

     

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