联合动力学与运动学的汽车状态估计

Vehicle State Estimation Based on United Dynamics and Kinematics

为了准确估计汽车的横向速度,利用七自由度汽车动力学模型、汽车运动学方程和Duoff非线性轮胎模型,建立包含噪声统计特性的汽车离散化动力学和运动学方程,提出2种方程联合的汽车状态联合估计算法。基于球面单形径向容积卡尔曼滤波(spherical simplex-radial cubature kalman filter,SSRCKF)和汽车动力学方程估计汽车的纵向速度和横向速度,以该纵向速度为量测输入,用容积卡尔曼滤波(cubature kalman filter,CKF)和运动学方程更精确地估计汽车的横向速度。利用CarSim及MATLAB/Simulink建立估计算法模型,并验证联合估计算法的有效性。该联合估计算法在双移线工况中横向速度的估计精度较SSRCKF提高了4.73%。

In order to accurately estimate the lateral velocity of the vehicle,the dynamics model,kinematics model and nonlinear tire model of the seven-degree-of-freedom vehicle are used to establish the discretized dynamics and kinematics equations,which include the characteristics of noise statistics.And a new estimation algorithm of the vehicle state is proposed,which unites the two equations.This algorithm estimates the vehicle longitudinal velocity and lateral velocity based on the spherical simplex-radial cubature Kalman Filter(SSRCKF)and the dynamics equation of the vehicle.The longitudinal velocity,as input,is used to estimate the lateral velocity more precisely based on the cubature Kalman filter(CKF)and the kinematics equation.The CarSim and MATLAB/Simulink are used to establish the algorithm model of estimation,and then the united estimation algorithm is verified.Compared with SSRCKF,the estimation accuracy of the lateral velocity by the united estimation algorithm is increased by 4.73%in the condition of the double moving line.