A Comparison of Two Soft-Sensing Methods for Estimating Vehicle Side Slip Angle

Two soft-sensing methods which are neural network and Kalman filter for estimating vehicle side slip angle are compared. A radial basis function (RBF) neural network based soft-sensing model is proposed to estimate vehicle side slip angle in driver-vehicle closed-loop system. Vehicle side slip angle is considered as mapping of time series of yaw rate and lateral acceleration which are easily measured, the nonlinear mapping relationship of the three state parameters is established through neural network. In addition the method based on Kalman filter is also given. The results of comparison between estimation and measurement show that the neural network method proposed in this paper has higher accuracy and less computation requirement. It can provide theoretical guidance for design of estimator in vehicle stability control system.