基于MSIF-AFKF算法的大直径回转体动态位置检测方法  

Dynamic Position Detection Method for Large Diameter Revolving Part Based on MSIF-AFKF Algorithm

作　　者：雷少坤[1] 谷文韬[1] 周少伟[1] 冯新[1] 

机构地区：[1]西北工业大学机电学院,西安710072

出　　处：《机械工程学报》2013年第5期116-122,共7页Journal of Mechanical Engineering

基　　金：国家自然科学基金(50875210);西北工业大学研究生创业种子基金(z2012049)资助项目

摘　　要：大直径回转体类零件动态位置变量不易准确测得或测量成本较高,而动态位置的准确获取对关键回转体类零件运动控制有着重要的意义。提出一种大直径回转体动态位置实时检测新方法,该方法针对回转体匀速、匀加速及变加速的不同转动状态,在传统融合算法的基础上引入自适应渐消因子,建立多传感器自适应渐消Kalman滤波融合(Multisensor informationfusion based on adaptive fading Kalman filter,MSIF-AFKF)算法,融合多组光栅传感器信息,对回转体不同转动状态的位置参数进行联合估计。仿真表明,与基于传统融合算法的检测方法相比,基于MSIF-AFKF算法的检测方法具有更高精度的动态位置输出,将该方法应用于某型卧式铆接设备的床头床尾空心轴转角定位系统中,并进行试验验证,其结果与模拟仿真结果一致。The dynamic position variable of large diameter revolving part is not easy to measure precisely and cheaply, however, the accuracy of the dynamic position is of great significance to controlling the movement of the key revolving part. A novel real-time detection method is proposed for the dynamic position of large diameter revolving part rotating with constant velocity, constant acceleration or variable acceleration. In order to estimate the position variable, multisensor information fusion based on adaptive fading Kalman filter （MSIF-AFKF） algorithm, which combines traditional information fusion algorithm with an adaptive fading factor, is established to fuse multi-grating sensor information. The method based on MSIF-AFKF algorithm is simulated by Monte Carlo and also used to detect the rotation angle position in the bed head and bed tail of horizontal riveting machine, the both results show that the method can significantly improve the precision.

关 键 词：大直径回转体 动态位置检测 自适应渐消因子 KALMAN滤波 

分 类 号：TB922[一般工业技术—计量学]