Road Adhesion Coefficient Estimation Based on Adaptive Unscented Kalman Filter

The road adhesion coefficient is significant in vehicle safety control systems. The road adhesion coefficient plays a key role in vehicle safety systems. To estimate this parameter, this paper formulates a seven-degree-of-freedom vehicle dynamics model. The normalization of tire force is achieved by integrating the Dugoff tire model. Based on this, an adaptive unscented Kalman filter (AUKF) algorithm is proposed. To reduce the 'non-local effect' in the sampling process, a proportional correction coefficient is used in the unbiased transformation. Additionally, an adaptive coefficient is incorporated into the standard unscented Kalman filter (UKF), and the updated covariance matrix is utilized to dynamically regulate the filtering gain. This enhancement significantly improves the algorithm's adaptability to the evolving states. Joint simulations of various road conditions are conducted using Carsim and Simulink. Experimental results indicate that the proposed adaptive unscented Kalman filter algorithm can reduce computational complexity and improve convergence speed while maintaining algorithm accuracy.