Compensation for FOG temperature drift based on adaptive neuro-fuzzy inference
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摘要: 温度漂移是影响光纤陀螺精度的重要因素之一。在对光纤陀螺温度漂移特性进行实验分析的基础上，对零偏温度漂移进行了多项式拟合补偿。为了解决传统曲面拟合方法无法精确描述标度因数温度漂移与温度、转速之间的关系导致其补偿精度低的问题，提出了一种基于自适应网络模糊推理的光纤陀螺温度漂移补偿新方法。该方法基于模糊逻辑，结合最小二乘和误差反向传播混合算法，设计了自适应网络模糊推理系统，从而有效提高了光纤陀螺温度漂移补偿精度。实验结果表明，在-30～60 ℃温度范围和-165～165 （）/s 载体角速率范围，应用新方法对光纤陀螺温度漂移进行补偿，得到的训练误差均方根不超过0.003 （）/s，预测误差均方根不超过0.005 （）/s。Abstract: The temperature drift is one of main factors influencing on the precision of a fiber optic gyroscope (FOG). The characteristic of FOG temperature drift was analyzed through temperature experiments. The zero bias temperature drift was compensated using the polynomial fitting method. To deal with the problem of poor compensation precision caused by the fact that the traditional surface fitting method can not describe the relationship between the scale factor temperature drift and the temperature or the angular rate accurately, a novel compensation approach for FOG temperature drift was proposed based on the adaptive neuro-fuzzy inference method. Based on the fuzzy logic, the approach combines the least square method with the back-propagation hybrid optimization algorithm to design an adaptive neuro-fuzzy inference system, so the compensation precision was improved effectively. The experiment results show that the training error root mean square and the predicted error root mean square of the new compensation approach are less than 0.003()/s and 0.005()/s respectively in the temperature range from -30℃ to -60℃ and the angular rate range from -165()/s to 165()/s
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Key words:
- FOG /
- temperature drift /
- adaptive neuro-fuzzy inference /
- scale factor /
- zero bias /
- surface fitting
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