针对小型光电编码器长周期误差成因及分布规律复杂的特点, 提出了一种光电编码器长周期误差修正方法。建立了基于正交三角函数基的傅里叶神经网路误差修正模型, 将光电编码器输入输出间的非线性优化问题转化为线性优化问题。误差修正模型以高精度基准编码器输出值作为学习目标; 引进模拟退火策略的差分进化算法对网络进行训练, 保证了在训练的初始阶段具有较强的全局寻优能力和在训练后期具有较快的收敛速度和较高的精度。运用设计的方法对16位小型光电编码器进行了长周期误差修正处理, 实际测试显示: 编码器的峰值误差由45″~-175″减小到10″~-875″, 长周期标准偏差由修正前203″减小到修正后4″以下。结果表明提出的长周期误差修正方法提高了光电编码器的精度。

The causes of long-period error of a small photoelectric encoder and its distribution law were researched and a correction method for the long-period error of the small photoelectric encoder was proposed. A Fourier neural network error correction model was built firstly based on orthogonal trigonometric functions, and the nonlinear optimization problem between the input and output of the encoder was transformed to a linear optimization problem. By taking the output value of the high-accuracy benchmark encoder as the learning reference for the neural network model, an improved differential evaluation algorithm combined with simulated annealing strategy was applied to training of the neural network and to ensuring its global optimization search ability in the initial stage but fast convergence rate and high accuracy in the later period.The method was applied to the long period error correction test of a 16-bit small photoelectric encoder, and experimental results show that the peak errors of the encoder is reduced from 45″—-175″ to 10″—-875″ and the standard deviation of long-period errors is reduced from 203″ to lower than 4″.The results mean that the proposed long-period error correction method improves the accuracy of small photoelectric encoders.