关键词: 汽车, 状态估计, 模糊卡尔曼滤波, 自适应卡尔曼滤波

Abstract:

Accurate acquisition of vehicle states information is the premise of vehicle dynamic control system’s proper functioning.A 2-DOF vehicle model was established,and the combination algorithm of S-correction  AKF and fuzzy Kalman filter was proposed to estimate vehicle key states,fuzzy Kalman filter achieves real-time estimation of the time variable measurement noise covariance matrix by using the fuzzy controller which designed by real-time monitoring the ratio of  the actual variance and theoretical variance of the residuals,thus increasing the robustness of the algorithm when the measured noise changed over time.S-correction AKF algorithm was based on the non-divergence filter theory,and the real-time correction factor S was derived,then the estimation error covariance matrix was weighted directly.In general,the combination of two methods improves the robustness and estimation accuracy when process noise and measurement noise covariance matrix is inaccurate.Finally,the validity of the method was verified by ADAMS-based virtual test.

Key words: vehicle;state estimation;fuzzy Kalman filter;adaptive , Kalman filter(AKF)