Estimation of Road Adhesion Coefficient Using Interactive Multiple Model Adaptive Unscented Kalman Filter for 4WID Vehicles

The road adhesion coefficient has an important impact on the vehicle dynamics control perfor⁃ mance. In order to accurately obtain the road adhesion coefficient in real time and improve the estimation accuracy and convergence speed of the algorithm under different road surfaces and driving conditions,an interactive multiple model adaptive unscented Kalman filter(IMM-AUKF)based on the seven-degree-of-freedom vehicle dynamics mod⁃ el and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles. The algorithm first in⁃ troduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer,which up⁃ dates the measurement noise in real time and ensures the positive characterization of its covariance matrix,im⁃ proves the weight of the new observation data,and enhances the real-time tracking accuracy and stability of the algo⁃ rithm. Afterwards,the algorithm selects different observation variables to construct the longitudinal driving condi⁃ tion AUKF observer and the lateral-longitudinal coupling driving condition AUKF observer. And the IMM algorithm is also used to switch the observer model,so as to realize the algorithm's accurate estimation of the road adhesion co⁃ efficient under different driving conditions. The results of simulation tests on high/low attachment,joint and u-split roads and real vehicle road tests show that the proposed IMM-AUKF algorithm has higher estimation accuracy and faster convergence speed than the traditional UKF algorithm,and it can adapt to the real-time and accurate estima⁃ tion of the road adhesion coefficient under different driving conditions. © 2024 SAE-China. All rights reserved.