摘要
拖拉机驱动轮滑转率的精确估计对提高拖拉机作业效率及安全性,实现拖拉机驱动防滑控制具有重要意义。本文提出了多新息并行扩展卡尔曼滤波算法,融合了包括机器视觉在内的多个传感器信息,并通过在线统计多传感器的新息,引入D-S证据理论进行决策,修正测量噪声矩阵,从而实现拖拉机驱动轮滑转率的精确估计。仿真结果表明,相较于普通的卡尔曼滤波算法,本文提出的融合算法估计滑转率的精度更高,滑转率估计值的均方根误差从2.34%降低到1.45%,且对干扰信号不敏感。试验结果表明,在多组工况下本文所提出的多传感器信息融合算法相较于单一视觉法或雷达法对拖拉机驱动轮滑转率估计的平均绝对误差、均方根误差均有所降低,从而验证了所提出的算法满足拖拉机驱动轮滑转率的精确估计,可对后续实现拖拉机驱动防滑控制提供依据。
Accurate estimation of the slip ratio for tractor driving wheels is of great significance to improve the working efficiency and safety of tractors and realize the anti-skid control of tractors.A multi-innovation parallel extended Kalman filter algorithm was proposed,then through online statistics of multi-sensor innovations,D-S evidence theory was introduced to make decisions and correct the measurement noise matrix,which fused the information of multiple sensors,including machine vision,and then realized the accurate estimation of tractor driving wheels'slip ratio.The simulation results showed that compared with the common Kalman filter algorithm,the proposed fusion algorithm had higher accuracy in estimating the slip ratio,the root mean square error of the estimated slip ratio was reduced from 2.34%to 1.45%,and the proposed algorithm was insensitive to interference signals.The test results showed that under two different working conditions,the average absolute error and root mean square error of the proposed multi-sensor information fusion algorithm were lower than those of single vision method or radar method.It was verified that the proposed algorithm can accurately estimate the slip ratio for tractor drive wheels,and then provide research help for the follow-up realization of tractor drive anti-skid control.
作者
韩冰
朱少华
杜现旭
刘禹汐
李臻
朱忠祥
HAN Bing;ZHU Shaohua;DU Xianxu;LIU Yuxi;LI Zhen;ZHU Zhongxiang(College of Engineering,China Agricultural University,Beijing 100083,China;Key Laboratory of Soil-Machine-Plant System Technology,Ministry of Agriculture and Rural Affairs,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第S01期402-410,426,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(52072407、52175259)
关键词
拖拉机
滑转率估计
多传感器信息融合
tractor
slip ratio estimation
multi-sensor information fusion
