极限行驶条件下车辆质心侧偏角观测器设计

Vehicle Sideslip Angle Estimation Under Extreme Driving Condition

用非线性车辆模型线性化方法,设计了基于广义卡尔曼滤波器和广义龙贝格观测器的质心侧偏角估计算法.给出考虑了轮胎非线性特性的扩展二自由度车辆模型,用非线性最小二乘法拟合轮胎模型的参数;分析非线性模型的能观性,并通过局部线性化方法,将非线性模型在当前状态处线性化,并得到近似的线性模型;设计了广义卡尔曼滤波器和线性化龙贝格观测器,并证明观测器的稳定性;最后,通过典型的操纵稳定性试验,验证算法的有效性.极限行驶工况下采用非线性模型线性化的方法,能获得更高的估计精度.

Through linearizing nonlinear vehicle model, vehicle side-slip angle estimation algorithms based on generalized Kalman filter and generalized Luenberger observer are formulated. An extended 2-degree-freedom vehicle model considering tire nonlinear characteristics is firstly proposed, and the tire model parameters are obtained with nonlinear least square method. Through local linearization method, the nonlinear vehicle model is linearized at current working states and by using this linear vehicle model,generalized Kalman filter and generalized Luenberger observer for side-slip angle estimation can be developed. Finally the effectiveness of both algorithms is verified through different handling maneuvers. Under extreme driving situation, the proposed methods can provide much higher estimation precision.