Estimation of wheel-rail forces based on the STF-SCKF-NE algorithm

Estimation of wheel-rail forces (WRFs) is critical for the intelligent operation and maintenance of rail vehicles. Based on the improved square-root cubature Kalman filter (SCKF), a WRF estimator is designed. The strong tracking filter (STF) and noise estimator (NE) are introduced to enhance the robustness of the filter. Then an extended vehicle state transfer function that includes all WRFs and a complete vehicle dynamics state is developed. Simulation results indicate that the proposed estimator performs well in estimating WRFs in both time domain and frequency domains. The robustness of this estimator is substantiated through Monte Carlo experiments under random irregularity excitation and inaccurate algorithm parameters. Moreover, the effectiveness of the method in complex operating environments is verified.