Volume 50 Issue 11
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LIU Y H,HUANG Y,TAN H,et al. On-line prediction method of wing flexible baseline based on autoregressive model[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(11):3426-3433 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0865
Citation: LIU Y H,HUANG Y,TAN H,et al. On-line prediction method of wing flexible baseline based on autoregressive model[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(11):3426-3433 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0865

On-line prediction method of wing flexible baseline based on autoregressive model

doi: 10.13700/j.bh.1001-5965.2022.0865
Funds:  National Natural Science Foundation of China (61901431); Science and Technology Program of the State Administration for Market Regulation (S2021MK0236)
More Information
  • Corresponding author: E-mail:huangy@bjjl.cn
  • Received Date: 30 Oct 2022
  • Accepted Date: 12 Feb 2023
  • Available Online: 10 Mar 2023
  • Publish Date: 09 Mar 2023
  • 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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