基于径向基函数神经网络的高精度基准编码器误差补偿
Error compensation for high precision reference encoder based on RBF neural networks
-
摘要: 高精度角度基准编码器的角度误差,对小型绝对式光电编码器误差检测装置的测量精度有着重要的影响。影响基准编码器角度误差的因素众多,难以用准确的数学模型来描述。为此提出一种通过径向基函数神经网络进行误差修正的方法。首先,为增加基准编码器检测采样点,使用多种多面体对基准编码器进行检测,并将误差合成在同一坐标曲线上。然后,利用检测误差结果作为训练样本,建立径向基函数神经网络模型,使其输出逼近真实角度。最后,通过补偿电路的设计,对小型编码器误差检测装置的基准编码器进行补偿。实验表明,补偿电路的处理速度快,实现简单,不受算法复杂度影响。补偿后的编码器精度提高了2 倍,有效改善了检测装置的检测精度。Abstract: The angle errors of high precision reference encoder deeply influence the precision of small size absolute photoelectric encoder's detection equipment. There are many factors that influence encoder's errors which can't be described by mathematical model. So in this paper, a method using RBF networks was proposed to amend errors. Firstly, the method used several kinds of polyhedron to detect the reference encoder with collimator, so that it could get more points of errors, and the errors were composed in one coordinate curve. Secondly, the RBF networks were built by using the result of error detection, in order to make their outputs closed to the true angle. Lastly, the reference encoder applied in small size absolute photoelectric encoder's detection equipment was compensated by the circuit of compensation. The experiments show that the circuit is fast, convenient and not influenced by the complexity of networks. The precision of encoder is improved 2 times, and this method improves the precision of detection equipment.
下载:
