| 吴琨,赵振义,范恒祯.机翼一体化天线变形重构标定方法研究[J].电子测量与仪器学报,2024,38(6):15-24 |
| 机翼一体化天线变形重构标定方法研究 |
| Research on deformation reconfiguration calibrationmethodof wing integrated antenna |
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| DOI： |
| 中文关键词: 机翼一体化天线 标定 主成分分析 自构架模糊网络 |
| 英文关键词:wing integrated antenna error calibration method principal component analysis self-construction fuzzy network |
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| 中文摘要: |
| 针对传统的误差标定方法存在网络训练速度迟缓、生成规则数量多且泛化能力不足的问题,本文提出了一种基于主成分分析和自构架模糊网络（PCA-SCFN）的标定方法，实现了机翼一体化天线的实时高精度变形重构。首先,基于逆有限元方法（iFEM）建立了位移-节点自由度误差模型,并通过单调快速迭代收缩阈值算法(MFISTA)对逆问题进行求解;其次,引入了PCA降维方法降低应变维度，从而简化训练网络复杂度；再次，对小样本训练集进行非均匀有理B样条(NURBS)拟合实现数据扩充，提高网络泛化能力并降低噪声对训练集的影响;最后，基于三角形隶属函数（MF）和Takagi-Sugeno（T-S）模糊模型进行自构架模糊网络(SCFN)训练获得模糊规则。机翼加载实验结果表明,基于PCA-SCFN的标定方法具有更快的训练速度和更少的规则数量，同时能够获得更高的重构精度。当机翼负载80 N时，结构最大变形为-134.36 mm,最大重构误差仅为0.46 mm,SCFN训练时间仅为9.715 s,规则数量最多仅有121条。因此，基于PCA-SCFN的标定方法是一种能够应用于机翼变形监测的有效方法。 |
| 英文摘要: |
| To deal with the issues of slow network training speed, large number of fuzzy rules and insufficient accuracy of traditional error calibration methods, a calibration method based on Principal Component Analysis and Self-Construction Fuzzy Network (PCA-SCFN) is proposed in this paper to realize real-time high-precision deformation reconstruction of integrated wing antennas. Firstly, a displacement-node degree of freedom error model is established based on the inverse finite element method (iFEM), and the inverse problem is solved by the monotone fast iterative shrinkage thresholding algorithm (MFISTA). Secondly, the PCA dimensionality reduction method is introduced to simplify the training network complexity by reducing strain dimensions. Thirdly, non-uniform rational B-spline (NURBS) fitting is applied to the small-sample training set to expand the data, enhancing network generalization and reducing the influence of noise on the training set. Finally, the SCFN is trained based on triangular membership functions (MF) and Takagi-Sugeno (T-S) fuzzy model to obtain the fuzzy rules. The results of wing loading experiment show that the PCA-SCFN-based calibration method can greatly improve the reconstruction accuracy, and at the same time, it has faster training speed and fewer rules. For a load of 80 N, the maximum reconstruction error is only 0.46 mm when the maximum deformation of the structure is -134.36 mm, and the training time of the SCFN is only 9.714 s, and the number of rules is only 121 at most. Therefore, the calibration method based on PCA-SCFN is an effective approach that can be applied to wing deformation monitoring. |
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