Parameter Estimation of the Distributed Drive Mining Dump Truck Based on SH-AUKF

This paper proposes an enhanced adaptive unscented Kalman filter (SH-AUKF) method based on the Sage–Husa algorithm to address the issue of insufficient estimation accuracy for state parameters and road adhesion coefficients in distributed drive mining dump trucks under complex mining conditions. By integrating a seven-degree-of-freedom vehicle dynamics model with the Dugoff tire model, a collaborative observer is constructed for estimating state parameters and the four-wheel road adhesion coefficient. Through joint simulation verification using Trucksim–Matlab 2025b, it was demonstrated that under sinusoidal steering, step steering, and varying road adhesion coefficients (0.3~0.7), the root mean square error (RMSE) of longitudinal vehicle speed, slip angle, and yaw rate estimation using SH-AUKF was significantly reduced compared to the traditional UKF. Additionally, the estimation error of the four-wheel road adhesion coefficient was decreased by 8~26%. This has significant application value for improving the automation level of mining transportation.