改进自适应卡尔曼滤波的拖拉机驱动轮滑转率估计

Estimation of Tractor Driving Wheel Slip Rate Based on Improved Adaptive Kalman Filtering

针对拖拉机驱动轮滑转率估计中存在的精度低和实时性差等问题,建立了拖拉机滑转率测量系统的模型。对传统Sage-Husa自适应卡尔曼滤波算法,从计算步骤简化和噪声估计简化两方面进行了改进,编写了MATLAB程序并与多种算法进行了对比分析。仿真结果表明:改进的变结构Sage-Husa自适应数据融合算法能估计噪声的统计特性,在不进行误差统计试验的前提下,实现了对拖拉机驱动轮滑转率的实时准确估计。驱动轮滑转率在白噪声的干扰下鲁棒性较好,平均误差为中值滤波算法的16%左右。

To solve the problem of low accuracy and poor real-time in estimating tractor driving wheel slip rate,a tractor slip rate measurement system model was established. The traditional Sage-Husa adaptive Kalman filtering algorithm was improved by calculation step simplification and noise estimation simplification. The MATLAB program of the improved algorithm was written and comparied with other algorithms. The simulation results show that the improved variable structure Sage-Husa adaptive data fusion algorithm can estimate the statistical property of noise and achieve the real-time and accurate estimation of tractor driving wheel slip rate without prior error statistical experiment. The sliding rate estimated by the improved adaptive Kalman filtering algorithm is robust under white noise jamming. The average error of the sliding rate estimated by the improved adaptive Kalman filtering algorithm is about 16% of that estimated by the median filtering method.