He, J., Yang, X.X., Zhang, C.F. and Xiao, M.J. (2023) Sliding Mode Consistency Tracking Control of Multiple Heavy Haul Trains Under Input Saturation and Safety Distance. Constraints, Journal of the Franklin Institute, 360, 9028-9049.
https://doi.org/10.1016/j.jfranklin.2022.07.003

has been cited by the following article:

• TITLE: Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

  AUTHORS: Shuai Qian, Lingshuang Kong, Jing He

  KEYWORDS: Heavy-Duty Train, Kiencke Model, Quadratic Programming, Time-Varying Forgetting Factor, Granger Causality Test

  JOURNAL NAME: Journal of Transportation Technologies, Vol.14 No.1, January 23, 2024

  ABSTRACT: A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.