Vehicle lateral state estimation with square root cubature Kalman filter under maximum correntropy criterion

Abstract

In vehicle lateral stability control, accurately estimating the vehicle’s lateral state is crucial. Vehicle state estimation is influenced by model accuracy, filter algorithms, and sensor accuracy, which are typically assumed to be affected by Gaussian noise. However, non-Gaussian noise often interferes with sensors in actual driving conditions, leading to decreased estimation accuracy and affecting subsequent stability analysis and control research. Traditional Kalman filters exhibit low accuracy and robustness under non-Gaussian noise conditions. This paper proposes the Maximum Correntropy Criterion Square Root Cubature Kalman Filter algorithm (MCCSCKF). By fusing signals from multiple vehicle sensors, the MCCSCKF processes non-Gaussian noise using the Maximum Correntropy Criterion and integrates it with the Square Root Cubature Kalman Filter to estimate the sideslip angle and yaw rate. The accuracy and robustness of the MCCSCKF algorithm are verified through comparisons with traditional Kalman filter variants under double-shift conditions at high-speed, low-adhesion, and fishhook conditions at medium-speed, low-adhesion in the MATLAB/Simulink co-simulation platform. Under high-speed conditions, the maximum estimation error of the sideslip angle using MCCSCKF is 0.003 rad, with a root mean square error (RMSE) reduction of 38.8%, significantly better than the UKF’s 90.5%. Under medium-speed conditions, the maximum sideslip angle error is 0.002 rad, with an RMSE of 12.2%. Finally, in real-vehicle tests using the VBOX-3iSL, the maximum sideslip angle error of the MCCSCKF is 0.0025 rad, with an RMSE of 9.7%, representing a 20.5% reduction compared to the SCKF, validating its superiority and robustness.