Estimation of the parameters of a railway vehicle suspension using model-based filters with uncertainties

Abstract

This paper presents two types of extended Kalman filter (EKF) and two types of unscented Kalman filter (UKF) based on vertical railway vehicle models for parameters estimation of secondary suspensions. Due to track irregularities, the random vertical velocity of the track can be approximated as a zero-mean Gaussian white noise and it is used to excite the dynamic model of the railway vehicle. Under this approximation, the variance of the vertical velocity of the track, which is affected by the track roughness level and vehicle velocity, can introduce uncertainty into the system. Based on the random track irregularity, two cases are proposed to determine how the track irregularities enter the system. One case uses the vertical velocity and displacement of the track as inputs of the system and assumes that the state variables are corrupted by the Gaussian noises. The other case assumes that the vertical velocity of the track is the process noise of the system. Based on these two cases, two types of EKF and UKF are developed to estimate the parameters of the secondary suspensions. In order to study the performances of the proposed EKFs and UKFs, several simulation experiments using linear and nonlinear model are carried out that consider the uncertainties of the random track.