圆光栅角度传感器的误差补偿及参数辨识

Error compensation and parameter identification of circular grating angle sensors

基于正弦函数和粒子群算法提出了一种误差补偿及参数辨识方法,用于提高圆光栅角度传感器的测量精度。使用光电自准直仪和金属多面体对圆光栅角度传感器的测量误差进行了离散标定,通过对标定数据的频谱分析,发现传感器测量误差主要由几种不同频率的正弦函数信号组成,由此提出了一种基于正弦函数的圆光栅角度传感器误差补偿模型。补偿模型中包含7个待定常量,本文采用粒子群算法求解这7个待定常量以克服最小二乘法无法收敛的问题。以待定常量为粒子位置坐标,以平均误差为适值函数,建立了一种基于粒子群算法的参数辨识模型,并根据参数辨识模型求出最优的待定常量。应用补偿模型对关节臂式坐标测量机的6个圆光栅角度传感器测量误差进行了补偿,结果表明:补偿后各角度传感器的平均测量误差减小了约398～1102.5倍,大大地提高了传感器的测量精度。

An error compensation and parameter identification method based on sine function and particle swarm optimization was presented to improve the measurement accuracy of circular grating angle sensors. The measurement errors of the sensors were calibrated discretely by a photoelectric autocolli- mator and a metal polyhedron. By analyzing the calibration data with the Fast Fourier Transform （FFT）, it was found that the measurement errors of the sensors are composed mainly of the sinusoidal signals with different frequencies. Thus an error compensation model consisting of seven constants to be determined was presented based on sine functions. Furthermore, by taking these constants as the location coordinates of particles and the average error as the fitness function, one identification model based on particle swarm optimization was built to calculate the constants in the compensation model. Finally, the compensation method was used to compensate the errors of the sensors in an articulated arm coordinated measuring machine. The experimental results show that the average errors of the sensors are reduced about 398-1 102.5 times after compensation.