Sideslip Angle Estimation for Mining Trucks Based on Real-Time Cornering Stiffness Identification

High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness of the front, middle, and rear axles is identified and updated in real time using MHE through a forgetting-factor least squares method based on yaw rate and lateral acceleration data within a fixed-length time window. The updated stiffness is then incorporated into the SCKF for accurate estimation of the sideslip angle. This sequential process effectively establishes a coupling between the estimation of the two parameters, forming an integrated joint estimation mechanism. The proposed framework is validated on the TruckSim–Simulink co-simulation platform, and the results confirm its superior accuracy and robustness, demonstrating its potential to improve the safety and control performance of MT.