Sideslip Angle Fusion Estimation Method of Three-Axis Autonomous Vehicle Based on Composite Model and Adaptive Cubature Kalman Filter

A sideslip angle fusion estimation strategy of the three-axis vehicle based on an adaptive cubature Kalman filter (ACKF) is investigated in this article. According to the dynamics model, kinematics model of the three-axis vehicle, and considering the influence of tire nonlinearity, the vehicle state estimators under different conditions are designed by using the ACKF algorithm. The dynamic-model-based estimator with linear tire model, a dynamic-model-based estimator with nonlinear tire model, and a kinematical-model-based estimator (KE) are proposed, then, according to the application characteristics of different estimators, a fusion estimation strategy of vehicle sideslip angle based on adaptive fuzzy weight controllers is designed, so as to improve the overall estimation accuracy by integrating the advantages of the three estimators. The simulation and experimental results show that the presented fusion estimation strategy can effectively improve the estimation accuracy of vehicle sideslip angle, and the comprehensive estimation accuracy reaches 94.37%.