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摘要:
机翼柔性动态形变制约分布式位置姿态系统（POS）传递对准精度的提升，光纤光栅传感器可精确测量柔性基线，然而光纤光栅传感器测量的应变转化为基线过程需要时间，这导致测量得到的基线不能实时用于传递对准。针对该问题，在光纤光栅传感器柔性基线测量的基础上，提出基于自回归模型的柔性基线在线预测方法。利用过去一段时间内实测应变值转换的基线数据来超前预测当前时刻的基线值，用于子节点实时传递对准量测匹配误差补偿，其中，模型参数可随输入实测基线数据在线递推更新，使得基线预测更加精确。在模拟机翼平台上进行振动实验，结果表明：所提方法实现了基线的精确预测，预测误差在0.051 mm之内，且具有很强的实时性。
Abstract:The flexible dynamic deformation of the wing restricts the improvement of transfer alignment accuracy for distributed position and orientation system（POS）. The fiber grating sensor can accurately measure the flexible baseline. However, the process of transforming the strain measured by the optical fiber grating sensor into the baseline takes extra time, which results in that the measured baseline cannot be used for transfer alignment in real time. This paper proposes an online prediction method of the flexible baseline based on the autoregressive model in order to solve this problem. The method is based on the measurement of the flexible baseline of the fiber grating sensor. By updating the model parameters recursively online with the measured baseline data as input, the model can predict the baseline with greater accuracy. Finally, the vibration experiment is carried out on a simulated wing platform. The experimental results show that the method proposed in this paper achieves accurate baseline prediction, the prediction error is within 0.051 mm and has strong real-time performance.
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Key words:
- array synthetic aperture radar /
- transfer alignment /
- fiber grating /
- autoregressive model /
- baseline
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图 4 4 Hz条件下不同振幅各阶模型的AIC值变化
Figure 4. AIC change of each order model with different amplitudes under the condition of shaking frequency 4 Hz
图 5 振幅为10 mm条件下不同频率各阶模型的AIC值变化
Figure 5. AIC changes of each order model with different frequencies under the condition of amplitude of 10 mm
图 9 不同振动频率下柔性基线变化曲线
Figure 9. Flexible baseline change curves under different vibration frequencies
表 1 机翼模型关键技术参数
Table 1. Key technical parameters of wing model
翼型长度/mm 翼根弦长/mm 翼尖弦长/mm 最大厚度/mm 根梢比 2700 320 240 21 0.75 
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表 2 机翼抖动的前4阶模态
Table 2. The first fourth modes of wing flutter
阶数 抖动频率/Hz 最大位移/mm 模态类型 1 2.217 432.89 横向弯曲 2 11.641 455.34 横向弯曲 3 30.860 466.91 横向弯曲 4 34.363 28.34 侧向扭曲
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表 3 不同模型阶数下模型参数辨识速度
Table 3. Identification speed of model parameter under different model orders
模型阶数 耗时/s 模型阶数 耗时/s 1 0.0060 11 0.0090 3 0.0065 13 0.0108 5 0.0073 15 0.0134 7 0.0081 17 0.0152 9 0.0087 19 0.0201
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表 4 不同模型阶数的预测误差
Table 4. Single step prediction errors for model different orders
模型阶数 单步预测误差/mm 3 0.0060 5 0.0056 13 0.0037 19 0.0040
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表 5 基线预测误差
Table 5. Baseline prediction errors
柔性基线采样序列 实测基线值/mm 预测基线值/mm 误差/mm 0～200 300.448 300.499 0.051 201～400 300.431 300.479 0.048 401～600 300.424 300.472 0.048 601～800 300.411 300.458 0.047 801～ 1000 300.414 300.462 0.048
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