基于PSD的轴系对中测试系统非线性校正方法研究

Research of nonlinear correction method in shafting alignment test system based on PSD

针对轴系对中测试系统的核心器件—位置敏感探测器(PSD)存在较大非线性误差、PSD输出坐标不能正确反映入射光点实际位置的问题,为提高对中的测试精度,设计了基于PI微位移平台的在线校定装置,提出了基于BP神经网络的非线性校正算法,进行了数据采集和非线性校正实验,并对采集数据和校正后数据分别进行了Matlab仿真分析。研究结果表明,基于BP神经网络的算法实用高效,采用该算法校正后的PSD线性度误差均低于3.9μm,极大地减少了非线性的影响,使得B区的线性度和数据置信度得到了大幅改善,在不增加成本和设备复杂度的前提下,有效提高了整个对中系统的精度。

Aiming at the large nonlinear error of position sensitive detector（PSD）, which is the core device of shafting alignment test system, output coordinate of PSD cannot correctly reflect the actual location of the incident light point, in order to improve the alignment test accuracy, the online calibration device based on the PI micro displacement platform was designed, the nonlinear correction algorithm based on BP neural network was presented, tbe data acquisition and nonlinear correction experiment was proceeded, acquired data and corrected data were simulated and analyzed by Matlab, respectively. The results indicate that the algorithm based on BP neural network is practical and efficient, after correction using this algorithm by the linearity error of PSD is less than 3.9 μm. The influence of nonlinear is largely reduced, so that the linearity and the data confidence of B area are greatly improved. On the premise of not increasing the cost and device complexity, the accuracy of the entire alignment system are effectively increased.