Estimation of Vehicle Sideslip Angle and Tire Cornering Stiffness Considering Mass Mismatch

As vehicles are utilized for transportation, their actual load is changeable, and the total mass is not remaining constant. Mismatch in mass parameters can lead to inaccuracies in the vehicle model, thereby diminishing the estimation accuracy of crucial state parameters like tire cornering stiffness (TCS) and sideslip angle (SA). To tackle these issues, this research proposes a co-estimation algorithm for the front and rear TCS and SA of vehicles based on Strong Tracking Unscented Kalman Filter (STUKF). Integrating the strong tracking concept into the Unscented Kalman Filter (UKF) by introducing a fading factor matrix, and real time adjustment of state error covariance matrix based on residual sequence orthogonality criterion to achieve swift correction of state estimation vector. To assess the efficacy of the STUKF algorithm under different road conditions, this research select different road friction-coefficients and compare the results with those obtained from EKF and RLS algorithms. The findings illustrate that the STUKF algorithm has enhanced the accuracy of estimation and demonstrates strong anti-interference capabilities for different road conditions and strong robustness to state mutations.