改进全自适应遗传算法的加速度计新标定方法  

Research on New Calibration Method of Accelerometer Based on Improved Fully Adaptive Genetic Algorithm

作　　者：于海燕[1] 李宇[2] YU Haiyan;LI Yu(School of Information Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China;School of Electronic and Electrical Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China)

机构地区：[1]郑州科技学院信息工程学院,郑州450064 [2]郑州科技学院电子与电气工程学院,郑州450064

出　　处：《实验室研究与探索》2022年第8期81-84,138,共5页Research and Exploration In Laboratory

基　　金：河南省高等学校重点科研项目(21B880041)。

摘　　要：针对当前使用的加速度计标定方法复杂、转台设备使用成本高、应用效果不佳等问题,设计了一种简捷快速标定测试及提高惯性测量单元精度的改进型自适应遗传算法的新型标定方法。在传统遗传算法的基础上提出了全自适应遗传算法,通过对多个算子的迭代收敛确定全自适应遗传算法(TAGA)的最优。同时,针对算法的标定效果进行了实验验证,并与经典牛顿法进行了对比。实验结果表明相对于经典牛顿法,该设计算法的模值标准差减小了27.9%,模值误差波动范围0.41 mg,TAGA算法的标定效果优于经典牛顿法,从而验证了该方法的有效性,能适用于实际的导航中。Aiming at the problems of high complexity,high cost of turntable equipment and poor application effect of the current accelerometer calibration method,a new calibration method of improved adaptive genetic algorithm is designed in this study.The core part of this method is the improved adaptive genetic algorithm.Firstly,an improved genetic algorithm is designed based on the traditional genetic algorithm.Then,through the iterative convergence of multiple operators,the optimal of fully adaptive genetic algorithm(TAGA)is determined.Finally,the calibration effect of the algorithm is tested and compared with the classical Newton method.The experimental results show that the calibration effect of TAGA algorithm is better than that of classical Newton method.According to the calibration experimental results,compared with the classical Newton method,the modular standard deviation of the designed algorithm is reduced by 27.9%and the fluctuation range of modular error is 0.41 mg,which verifies the effectiveness of this method.

关 键 词：MEMS三轴加速度计 自适应遗传算法 模标定 

分 类 号：V241.62[航空宇航科学与技术—飞行器设计]