考虑轨迹预测补偿的履带车辆滑动参数估计方法

Slid parameter estimates for tracked vehicles with trajectory prediction compensation

由于履带与地面之间的相互作用力复杂,因此为履带车辆建立精确的模型较为困难。该文提出了一种基于轨迹预测补偿的双重无迹Kalman滤波(DUKF)的履带车辆滑动参数实时估计方法。上层无迹Kalman滤波(UKF)利用历史轨迹信息对滑动参数进行初步估计,将初步估计的滑动参数输入基于履带车辆瞬时转向中心(ICR)的车辆模型进行轨迹预测,结合轨迹预测相对位置的残差,通过下层UKF对初步估计的滑动参数进行补偿。基于RecurDyn与MATLAB/Simulink搭建仿真模型对所提出的方法进行了验证。仿真结果表明,与传统的UKF和扩展Kalman滤波(EKF)相比,DUKF能够在转变工况下进一步提升履带车辆滑动参数估计精度并减小轨迹预测误差。

Accurate motion models for tracked vehicles are difficult to build due to the complex interactions between the tracks and the terrain.A dual-layer unscented Kalman filter(DUKF)is developed to estimate the slip parameters in real time for tracked vehicles.An upper-layer unscented Kalman filter(UKF)is used first to estimate the slip parameters based on the historical trajectory information,which are then imported into the vehicle model based on the instantaneous centers of rotation(ICRs)to predict the forward trajectory.A lower-layer UKF is then used to correct the preliminarily estimated slip parameters based on the residual position of the trajectory prediction.The effectiveness of the DUKF is verified by simulations on RecurDyn and MATLAB/Simulink.The simulations show that the DUKF improves the accuracy of the slip parameter estimation and reduces the trajectory prediction errors with curvatures compared with predictions using the UKF and the extended Kalman filter(EKF).