Fusion Algorithm Design Based on Adaptive SCKF and Integral Correction for Side-Slip Angle Observation

Abstract

Vehicle side-slip angle is crucial for various vehicle active safety applications, but measuring it directly needs expensive measurement instruments and the vehicle nonlinear dynamics, parameters uncertainty, and sensor noise cause difficulties in its observation. Therefore, the accurate, affordable side-slip angle estimator is essential. Thus, a novel adaptive square-root cubature Kalman filter (ASCKF)-based estimator with the integral correction fusion is proposed. First, the square-root cubature Kalman filter (SCKF) parameters can be adjusted adaptively based on the vehicle dynamics states. Then, considering the unknown colored noises of sensors, the integral estimation is corrected by the damping item and zero-point-reset method and the integral value can compensate the estimation error caused by the vehicle nonlinear dynamics. Therefore, the accurate side-slip angle can be estimated by the adaptive fusion of the estimation and integral values. The simulation results and real-vehicle tests show that the proposed ASCKF-based fusion algorithm has better performance than both the traditional SCKF and ASCKF.