基于卡尔曼滤波融合的电机电流高精度检测与处理

High Accuracy Detecting and Processing of Motor Current Based on Kalman Filter Fusion

根据同步采样下跟踪系统电机电流波动规律,推导出基波电流可以等效为载波中点和起点处电流采样值,并分析了同步采样与非同步采样下电流谐波,得出同步采样可以有效减小载波二倍频率谐波及高次谐波。针对跟踪系统在高仰角跟踪时,电机电流大变化剧烈,电流检测精度下降的情况,将不同量程不同精度的电流传感器应用于电机电流检测中,使用集中式卡尔曼(Kalman)滤波融合技术融合多传感器电流数据,在传感器量程内实时地获得了较高的电流检测精度,避免了传统滤波算法带来的电流滤波延时。

Based on the analysis of motor current fluctuation under synchronous sampling mode. Deduced that the fundamental current could be equivalent to the current values sampled at the beginning and midpoint of carrier. Then the current harmonics under synchronous sampling mode and non-synchronous sampling mode was analyzed. It turned out to be that twice the carrier frequency harmonics and higher harmonics could be effectively reduced under synchronous sampling mode. The motor current was large and changed sharply when Opto-Electronic Tracking system operated in high elevation tracking mode. The situation would cause an accuracy droping in current detection. The paper applied current sensor of different range and different precision in motor current detection. Then the centralized Kalman filter fusion was been applied in multi-current sensor data fusion. Eventually this method results in a high current detection accuracy in sensor range. This method could be processed in real-time so as to avoid the current filter delay in traditional filter algorithm.