基于ASTUKF的分布式农业车辆路面参数辨识方法

针对分布式驱动农业车辆在路面参数辨识过程中，因路面环境变化出现的状态模型误差和时变噪声，导致辨识结果发散的问题，提出了基于自适应强跟踪无迹卡尔曼滤波(Adaptive strong tracking unscented Kalman filter, ASTUKF)的辨识方法。与传统内燃机农业车辆相比，分布式驱动可以直接获取驱动轮的状态信息，结合含有峰值附着系数和极限滑转率的μ-s曲线模型，建立了无迹卡尔曼滤波(Unscented Kalman filter, UKF)辨识算法的状态方程和量测方程。同时，将强跟踪滤波(Strong tracking filter, STF)和自适应滤波(Adaptive filter, AF)引入辨识算法，用以提高对多变环境的识别精度和鲁棒性，并采用奇异值分解(Singular value decomposition, SVD)解决了迭代过程中出现的非正定矩阵的问题。仿真试验结果表明，在突变噪声环境工况下，ASTUKF辨识结果可以快速收敛至目标值附近，且不受突变噪声的影响，各驱动轮峰值附着系数估计结果的平均绝对误差(Mean absolute error...

Road Parameters Identification Method for Distributed Agricultural Vehicle Based on ASTUKF

A method utilizing the adaptive strong tracking unscented Kalman filter (ASTUKF) was proposed to address the issue of divergent identification results caused by state model errors and time-varying noise resulting from changes in road environments during the terrain parameters identification of distributed drive agricultural vehicles. Compared with the traditional internal combustion engine agricultural vehicles, distributed drive agricultural vehicles can directly obtain state information of the driving wheel. And combining the μ-s model which contained adhesion coefficient and limit slip ratio, a state function and a measurement function of unscented Kalman filter (UKF) identification algorithm were established. At the same time, strong tracking filter (STF) and adaptive filter (AF) were introduced into the identification algorithm to improve identification accuracy and robustness against the changing environment, and singular value decomposition (SVD) was used to solve the problem of non-positive definite matrix in iterative process. The simulation test showed that under the condition of abrupt noise environment, the identification result of ASTUKF can quickly converge to target value, which was not affected by abrupt noise. Mean absolute errors (MAE) of the adhesion coefficient estimation results of each driving wheel were 0.0144, 0.0267, 0.0144 and 0.0267, respectively, and MAE of the limit slip ratio estimation results were 0.0025, 0.0028, 0.0025 and 0.0028, respectively. The real vehicle test showed that the 95% confidence interval of average identification result of ASTUKF can match the measured value on test road of cultivated and uncultivated road. The identification results of adhesion coefficient of the whole vehicle were 0.4061 (uncultivated road) and 0.3991 (cultivated road), and the identification results of limit slip ratio were 0.1484 (uncultivated road) and 0.3600 (cultivated road), which can provide a theoretical reference for the operation parameter perception of distributed electric agricultural vehicles.