Estimating wheel-road vertical load for nonlinear vehicle vibration system based on cubature Kalman filter

The vibration of vehicle is most governed by the load generated of the road roughness, which can affect comfort of vehicle and cause fatigue damage of the vehicle suspension. So knowledge of the load is very important for vehicle control systems to enhance vehicle stability and safety. During the process of the estimation, noise affects the identification accuracy, and maybe cause divergence. The cubature Kalman filter (CKF) has good accuracy performance, numerical stability and computational costs. So this paper presented a new methodology based on CKF and a recursive least-squares algorithm for estimating wheel-road vertical load. The state equations and measurement equations were established according to the second order vibration system, and RK4 was employed to discretize the continuous-time equations. The CKF was used to calculate the residual innovation sequences and the residual innovation sequences were used to calculate the vertical load. To verify the effectiveness of the identification method, numerical simulations of the five degrees of freedom vehicle vibration system subjected to white noises and four types of forces were employed. Simulation results validated and proved the feasibility of this approach.