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分析了光纤陀螺的温度特性,设计了大范围的温度测试,研究了不同温度和温度变化率对光纤陀螺输出的影响,研究了光纤陀螺在不同温度范围内的温度特性。为了提高温度误差补偿精度,根据陀螺温度特性将温度分为低、中、高三个区间,分别利用人工神经网络进行误差建模,提出了一种多模型分段拟合的新方法。根据建立的模型进行温度误差补偿,补偿结果表明,建立的模型能有效地减小了光纤陀螺的温度漂移,精度提高了一个量级。
The temperature characteristics of fiber optic gyroscope (FOG) are analyzed. A wide range of temperature tests are designed. The effects of different temperature and temperature rates on the output of FOG are studied. The temperature characteristics of FOG in different temperature ranges are studied. In order to improve the accuracy of temperature error compensation, the temperature is divided into low, middle and high temperature intervals according to the temperature characteristics of the gyroscope. The artificial neural network is used to model the error respectively. A new method of multi-model fitting is proposed. The temperature error compensation is carried out according to the established model. The compensation results show that the model can effectively reduce the temperature drift of the fiber optic gyroscope and improve the accuracy by one order of magnitude.