基于粒子群优化算法的光纤陀螺温度误差分段补偿方法

Segmentation compensation method for FOG temperature error based on particle swarm optimization algorithm

针对光纤陀螺的温度误差单一模型补偿方法适配性较差的问题,提出一种基于粒子群优化(PSO)算法的光纤陀螺温度误差分段补偿方法。此方法基于分段建模补偿的思想,在建模时加入温度和温度变化率影响因子,并引入PSO算法极值寻优,得到最优补偿函数。为了验证此方法的补偿效果,设计了-15℃~50℃区间内光纤陀螺温度实验,分别利用所提方法和传统方法对其温度误差进行补偿。试验结果表明,使用所提方法能够极大地降低温度误差,与传统算法相比,在保证补偿后陀螺零偏稳定性一致的前提下陀螺零偏均值降低了一个数量级,并且具有实时补偿性。

Aiming at the poor adaptability of single-model compensation method for fiber optic gyro(FOG)temperature error,a segmentation compensation method based on particle swarm optimization(PSO)algorithm is proposed.Based on the idea of piecewise modeling compensation,this method adds the influence factors of temperature and temperature change rate into the modeling,and introduces PSO algorithm to optimize the extreme value,so as to obtain the optimal compensation function.In order to verify the compensation effect of the proposed method,the FOG temperature experiment within-15℃to 50℃is designed,and the temperature error is compensated by the proposed method and traditional method,respectively.Experimental results show that the proposed method can greatly reduce temperature error is of real time compensation.Compared with the traditional algorithm,the zero offset average of FOG is reduced by an order of magnitude.