基于IAO的随钻MEMS加速度计误差参数识别方法

Error parameter identification method of MEMS accelerometers while drilling based on IAO

针对随钻测量中MEMS加速度计器件误差变化难以精确识别的问题,提出一种基于改进天鹰算法(IAO)的随钻MEMS加速度计误差参数识别方法。首先根据MEMS加速度计输出模型变换得到非线性误差函数,采用天鹰算法(AO)求解到非线性误差函数的最优值。在AO算法基础上,利用误差参数平均值和迭代次数改善AO算法开采参数的全局搜索能力,基于误差参数最优值和平均值构造径向因子提高AO算法误差参数识别精度。然后采用IAO算法极限逼近非线性误差函数的参数最优值,并通过采样点插值误差数据库完成误差参数校准。最后将IAO算法应用于识别加速度计误差参数,结果表明:IAO算法识别精度比PSO算法、SSA算法和AO算法提高了1~2个数量级,补偿后的加速度计输出误差明显减小。

Aiming at the problem that it is difficult to accurately identify the error changes of MEMS accelerometer in the measurement while drilling,an error parameter identification method of MEMS accelerometer while drilling based on the improved aquila optimizer(IAO)algorithm is proposed.Firstly,the nonlinear error function is obtained by transforming the output model of MEMS accelerometer,and the optimal value of the nonlinear error function is solved by the aquila optimizer(AO)algorithm.On the basis of AO algorithm,the average value of error parameters and the number of iterations are used to improve the global search ability of mining parameters of AO algorithm,and radial factors are constructed based on the optimal value and average value of error parameters to improve the accuracy of AO algorithm error parameter identification.Then,IAO algorithm is used to approximate the optimal parameters of the nonlinear error function,and the error parameters are calibrated through the sampling point interpolation error database.Finally,IAO algorithm is applied to identify the error parameters of the accelerometer.The results show that the recognition accuracy of IAO algorithm is 1~2 orders of magnitude higher than that of PSO algorithm,SSA algorithm and AO algorithm,and the output error of the accelerometer is obviously reduced after compensation.