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.