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为解决传统质心侧偏角估计误差较大以及实时性不高的问题,以某型多轮电传动车辆为研究对象,设计了一种基于无味卡尔曼滤波(Unscented Kalman Filter,UKF)的质心侧偏角估计方法,以车辆双轨3自由度模型和非线性轮胎模型为基础,建立基于UKF算法的非线性状态观测器,对车辆质心侧偏角进行估计。仿真结果表明:该方法能满足车辆在良好路面低速、高速蛇形运动以及低附着路面蛇形运动状态下的良好估计效果,为多轮电传动车辆质心侧偏角估计提供了一种有效的估计方法。
In order to solve the problem of large estimation error and low real-time accuracy of traditional centroid roll angle, a kind of multi-wheeled electrically-driven vehicle was selected as the research object. A centroid side based on Unscented Kalman Filter (UKF) Based on the two-track 3-DOF model and the nonlinear tire model, a nonlinear state observer based on UKF algorithm is established to estimate the vehicle center of mass roll angle. The simulation results show that this method can effectively estimate the vehicle’s behavior under low-speed, high-speed serpentine motions and low-adhesion pavement snake motions, and provides an effective estimation for the center of mass side slip angle of multi-wheeled electric vehicles method.