Adaptive Unscented Kalman Filter Approach for Accurate Sideslip Angle Estimation via Operating Condition Recognition - Liang Zhao; Jiawei Wang; Yingjia Hu; Liang Li (Machines)
This paper presents an innovative method for estimating vehicle sideslip angle by integrating a dynamic–kinematic coupled Unscented Kalman Filter (UKF) with an adaptive strategy that ensures accuracy across various surface conditions and operational scenarios. This research employs a two-degree-of-freedom vehicle kinematic model for state updates and constructs a vehicle dynamic model, utilizing parameters obtained from real vehicle calibration to monitor the system. Additionally, this paper thoroughly explores the performance characteristics and applicable conditions of both dynamic and kinematic models. It proposes reference speed factors, surface friction factors, and lateral characteristic factors to indicate the confidence levels of the two models under different operating conditions and address state estimation requirements across diverse scenarios. Thence, the adaptive strategy proactively adjusts the noise covariance matrix to achieve an optimal balance between the dynamic and kinematic models. The effectiveness of the adaptive UKF estimation strategy is validated through real vehicle tests conducted under various scenarios with differing friction coefficients and operational conditions. The results indicate that the proposed strategy surpasses existing approaches utilizing the Luenberger observer and UKF observer in all scenarios. Notably, on low-friction surfaces and during extreme maneuvers, the experimental results underscore the superior performance facilitated by the adaptive strategy.