Real-Time Wing Deformation Monitoring via Distributed Fiber Bragg Grating and Adaptive Federated Filtering

TL;DR

This paper introduces a new method using Fiber Bragg Grating sensors to monitor wing deformation and improve alignment accuracy in flight systems.

Contribution

A novel federated adaptive filtering algorithm based on allocation coefficients is proposed to enhance alignment accuracy in complex flight environments.

Findings

The federated adaptive filter improved pitch angle accuracy by 66.38% and position estimation by 75.67%.

The updated algorithm improved sub IMU pitch angle accuracy by 76.72% and position estimation by 63.51%.

Lever arm estimation accuracy was enhanced in the east and sky directions.

Abstract

To address the issues of decreased accuracy and poor stability in distributed transfer alignment caused by factors such as wing deflection and deformation in complex flight environments, this paper proposes a wing-distributed transfer alignment method based on Fiber Bragg Grating (FBG). This paper establishes a flexural deformation model based on FBGs, establishes a coupling angle model and a dynamic lever arm model, derives the motion parameter relationship model between the main and the sub-nodes, establishes the corresponding transfer alignment filter, and proposes a federated adaptive filter based on allocation coefficients and an updated federated adaptive filter. The results show that the federated adaptive filtering algorithm based on allocation coefficients improved the pitch angle accuracy of the Inertial Measurement Unit (IMU) by 66.38% and the position estimation accuracy by 75.67%, compared to traditional algorithms. The arm estimation accuracy was also improved in the east and sky directions. Compared with traditional algorithms, the updated federated adaptive filtering algorithm improved the pitch angle accuracy of the sub IMU by 76.72%, the position estimation accuracy by 63.51%, and the lever arm estimation accuracy.

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