基于多模型集成算法的光纤陀螺温度补偿及实现

Fiber Optic Gyroscope Temperature Compensation and Implementation Based on Multi-model Ensemble Algorithm

摘要:

为降低光纤陀螺因温度效应产生的零偏漂移,以基于最小二乘法的多项式补偿模型和经遗传算法优化后的BP神经网络模型(GA-BP)为基学习器,通过集成学习算法建立了光纤陀螺的温度补偿模型,并对补偿后的光纤陀螺进行在线温度补偿实验。实验结果表明,该模型在-40~+60℃温变环境下将光纤陀螺的全过程零偏漂移降低了85%以上,且补偿后的启动段零偏输出均值更接近零位。

Abstract:

To reduce the bias drift of fiber optic gyroscopes due to the temperature effect, a temperature compensation model of the fiber optic gyroscope was established using an ensemble learning algorithm based on a least squares polynomial model and back propagation (BP) neural network model optimized by a genetic algorithm (GA-BP). A temperature compensation experiment of the fiber optic gyroscope was conducted after online compensation. Experimental results show that the model reduces the bias drift of the fiber optic gyroscope by more than 85% in an environment with a temperature change of -40~+60℃, and the average bias output of the compensated starting section is closer to the zero position.