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基于自适应遗传算法的MEMS加速度计快速标定方法

高爽 张若愚

高爽, 张若愚. 基于自适应遗传算法的MEMS加速度计快速标定方法[J]. 北京航空航天大学学报, 2019, 45(10): 1982-1989. doi: 10.13700/j.bh.1001-5965.2019.0040
引用本文: 高爽, 张若愚. 基于自适应遗传算法的MEMS加速度计快速标定方法[J]. 北京航空航天大学学报, 2019, 45(10): 1982-1989. doi: 10.13700/j.bh.1001-5965.2019.0040
GAO Shuang, ZHANG Ruoyu. Rapid calibration method of MEMS accelerometer based on adaptive GA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 1982-1989. doi: 10.13700/j.bh.1001-5965.2019.0040(in Chinese)
Citation: GAO Shuang, ZHANG Ruoyu. Rapid calibration method of MEMS accelerometer based on adaptive GA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 1982-1989. doi: 10.13700/j.bh.1001-5965.2019.0040(in Chinese)

基于自适应遗传算法的MEMS加速度计快速标定方法

doi: 10.13700/j.bh.1001-5965.2019.0040
详细信息
    作者简介:

    高爽  女, 博士, 讲师。主要研究方向:惯性导航

    张若愚   女, 硕士研究生。主要研究方向:惯性导航

    通讯作者:

    高爽, E-mail: gaoshuang@buaa.edu.cn

  • 中图分类号: V241.62

Rapid calibration method of MEMS accelerometer based on adaptive GA

More Information
  • 摘要:

    微惯性测量单元(MIMU)的标定技术是低精度惯性导航领域中的重要研究方向,传统标定方法操作复杂,标定精度严重依赖转台精度。为解决大批量MIMU快速标定的问题,提出了一种基于自适应遗传算法(GA)的微机电系统(MEMS)加速度计快速标定方法,将加速度计标定问题转化为参数优化问题。首先,利用模观测原理构造目标优化函数;然后,分析系统可观测度确定最优标定编排方案;最后,采用全局搜索的自适应遗传算法优化标定参数。实验结果表明:与牛顿迭代法相比,标定精度提升1~3个数量级,运算速度提高61%。标定后解算的水平姿态角误差小于0.1°,可实现与传统标定方法相同量级的姿态精度,验证了所提方法的优越性和实用性。

     

  • 图 1  遗传算法流程

    Figure 1.  Flowchart of genetic algorithm

    图 2  静态多位置标定编排方案

    Figure 2.  Static multi-position calibration scheme

    图 3  MTi-1系列惯性测量组合

    Figure 3.  MTi-1 series inertial measurement unit

    图 4  适应度函数变化曲线

    Figure 4.  Curves of fitness function variation

    图 5  误差参数标定结果

    Figure 5.  Calibration results of error parameters

    图 6  水平姿态角误差曲线

    Figure 6.  Curves of horizontal attitude errors

    表  1  多位置下可观测性判别矩阵的秩和奇异值

    Table  1.   Rank and singular value of observable matrix under multi-position

    位置 奇异值
    3 8 29.861 29.713 29.562 29.405 29.405 29.405 4.220 2.969 1.715×10-7
    4 9 41.585 41.585 41.585 29.716 29.562 29.405 6 5.168 4.198
    5 9 41.585 41.585 41.585 41.585 41.585 29.564 6.708 6.708 5.968
    6 9 41.805 41.585 41.585 41.585 41.585 41.585 7.348 4 7.348 4 5.968 3
    7 9 51.106 46.513 41.693 41.585 41.585 41.585 7.937 2 7.819 6 6.006 3
    8 9 55.280 46.533 46.513 46.493 46.493 41.657 8.374 9 8.263 3 6.033 1
    9 9 59.063 55.107 50.992 46.596 46.493 46.493 8.649 9 8.394 6 6.451 9
    10 9 64.408 57.427 50.991 48.495 48.261 46.572 9.154 2 9.122 3 8.204 8
    11 9 68.476 58.829 55.023 50.931 50.931 47.303 9.882 8 9.832 8 9.463 6
    12 9 72.041 65.751 58.810 51.012 50.931 50.931 10.392 10.392 9.881 7
    13 9 72.041 67.576 58.813 58.810 52.764 50.951 10.816 10.765 10.605
    14 9 72.041 68.973 62.385 62.377 55.011 55.011 11.178 11.149 11.132
    15 9 72.041 68.973 65.779 62.394 62.394 62.377 11.458 11.456 11.439
    下载: 导出CSV

    表  2  本文方法与牛顿迭代法的标定结果对比

    Table  2.   Comparison of calibration results between proposed method and Newton's iteration

    误差参数 传统标定方法标定结果 牛顿迭代法 本文方法
    标定结果 相对误差/% 标定结果 相对误差/%
    Bx/(m·s-2) 0.116 46 0.116 29 -0.146 0.116 44 -0.017
    By/(m·s-2) 0.036 1 0.035 9 -0.554 0.036 107 0.019
    Bz/(m·s-2) 0.136 44 0.138 42 1.451 0.136 37 -0.051
    Sxx 9.805 18 9.805 2 0.000 204 9.805 9 0.007
    Syy 9.787 18 9.788 2 0.010 4 9.787 6 0.004
    Szz 9.771 07 9.811 5 0.414 9.771 1 0.000 3
    Mxy -0.005 8 0.276 8 × -0.006 42 10.69
    Mxz 0.005 22 0.405 1 × 0.002 42 -53.64
    Myx 0.006 07 —— —— —— ——
    Myz 0.003 93 -0.768 9 × 0.003 09 -21.37
    Mzx -0.003 6 —— —— —— ——
    Mzy -0.006 2 —— —— —— ——
    注:“×”表示无相对误差结果;“——”表示无此项安装误差。
    下载: 导出CSV

    表  3  本文方法与牛顿迭代法的运算时间对比

    Table  3.   Comparison of operational time between proposed method and Newton's iteration

    方法 运算时间/s
    本文方法 164.28
    牛顿迭代法 421.92
    下载: 导出CSV

    表  4  水平姿态角误差均值

    Table  4.   Mean of horizontal attitude errors

    方法 俯仰角误差/(°) 横滚角误差/(°)
    传统标定方法 0.015 0.109
    牛顿迭代法 0.24 -0.191
    本文方法 0.062 -0.051
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-29
  • 录用日期:  2019-04-26
  • 刊出日期:  2019-10-20

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