Abstract:

The road adhesion coefficient has an important impact on the vehicle dynamics control performance. 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 model and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles. The algorithm first introduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer， which updates the measurement noise in real time and ensures the positive characterization of its covariance matrix， improves the weight of the new observation data， and enhances the real-time tracking accuracy and stability of the algorithm. Afterwards， the algorithm selects different observation variables to construct the longitudinal driving condition 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 coefficient 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 estimation of the road adhesion coefficient under different driving conditions.

Key words: distributed four-wheel drive, road adhesion coefficient, interactive multiple model, adaptive unscented Kalman filter