A Novel Approach for Tire-road Friction Coefficient Estimation Based on Limited Memory Random Weighted Unscented Kalman Filter

Acceleration performance, braking performance, handling performance and ride performance are crucial for driving safety of vehicles. The accurate acquisition of tire-road friction coefficient (TRFC) is the key to the stable operation of vehicle chassis control systems. Compared with the direct measurement of TRFC by using expensive sensors, indirect measurement methods combined with vehicle dynamics models and advanced estimation filters are more cost-effective and suitable for severe traffic scenarios. In this paper, a novel limited memory random weighted unscented Kalman filter (LMRWUKF) is proposed to estimate the TRFC under different traffic conditions. First, a nonlinear three-degree-of-freedom vehicle dynamics model including longitudinal, lateral and yaw directions is established. Then, the longitudinal force and lateral force of the tires are normalized by analyzing the Dugoff tire model. Next, an improved unscented Kalman filtering algorithm combined with the limited memory filter and random weighted theory is used to estimate the TRFC. Finally, the co-simulation platform of MATLAB/Simulink and Carsim is built to verify the effectiveness of the LMRWUKF. The test results indicate that the estimation performance of LMRWUKF is better than that of traditional unscented Kalman filter.