汽车行驶状态参数的估计

介绍Sage-Husa自适应卡尔曼滤波算法和滤波估计流程,建立二自由度汽车模型,在模型中加入系统噪声和测量噪声,建立系统状态方程和观测方程。利用自适应卡尔曼滤波算法,对汽车质心侧偏角和横摆角速度进行估计,并进行转向盘转角正弦输入仿真分析,仿真结果表明两者的真实值和估计值吻合良好。利用自适应卡尔曼滤波算法对汽车行驶状态参数进行估计可以降低汽车的成本,是一种行之有效且具有工程应用价值的方法。

Estimation of Automobile Steering State Parameters

Sage-Husa adaptive Kalman filtering algorithm and filter flow were introduced.Vehicle model with two degrees of freedom was established,which added noises of system and measurement,and the state equation and observation equation of the model was established.Vehicle centroid sideslip angle and yaw rate were estimated using the adaptive Kalman filtering algorithm.The model was simulated with steering wheel sine-input.The simulation results show that the true numerical values and estimated numerical values tally well.The method of vehicle state parameters estimating using the adaptive Kalman filtering algorithm is a low-cost and feasible way with high engineering application value.