针对汽车底盘控制系统中一些关键汽车行驶状态难以准确直接测量以及测量成本较高的问题,在自适应卡尔曼滤波算法和强跟踪滤波算法的基础上,提出基于自适应强跟踪滤波器的汽车行驶状态估计方法.结合纵向、侧向和横摆3自由度非线性汽车模型,将其应用于汽车行驶状态的软测量之中,并与扩展卡尔曼滤波算法进行比较分析.通过Carsim和Matlab/SIMULINK联合仿真的双移线试验的结果表明,在非线性区域内,自适应强跟踪滤波器能快速、准确跟踪汽车状态.该算法在估计精确度、跟踪速度、抑制噪声等方面均优于扩展卡尔曼滤波算法,满足汽车状态估计器的软件性能要求.%Some key vehicle states in vehicle chassis control system are difficult to measure directly or in low cost. Based on adaptive Kalman filter ( AKF) and strong track filter (STF) , a soft computing method consisting of adaptive strong track filter (ASTF) was proposed. By using a nonlinear 3 degree-of-freedom vehicle model including longitudinal motion, lateral motion and yaw motion, a state estimation algorithm was established and applied to vehicle state estimation. Comparison had been made between extended Kalman filter (EKF) and ASTF. A virtual double lane change test had been carried out on Carsim and Matlab/Simulink co-simulation. The results show that ASTF can follow the vehicle state quickly and pre cisely. ASTF is better than EKF on the estimating accuracy, tracking speed and restraining noise. It is proved that ASTF can satisfy the requirements of vehicle state estimation.
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