An Adaptive Neuro-Fuzzy Inference Approach of AOA/AOS Data Fusion for Small Fixed-Wing UAV

: Accurate measurement of angle of attack (AOA) and angle of sideslip (AOS) is crucial for ensuring the safe operation of fixed-wing unmanned aerial vehicles (UAVs) and conducting reliable flight performance evaluations. Given the limited payload capacity of small-sized UAVs, lightweight wind vane probes are commonly employed. Although installing wind vane sensors at the nose typically yields accurate measurements, this placement is impractical for UAVs with front-mounted propellers. An alternative is to position the sensors beneath the wing, but this configuration introduces measurement inaccuracies due to propeller-induced slip-stream and fuselage obstruction. To address these challenges, estimating AOA and AOS using inertial data through the unscented Kalman filter (UKF) offers a more robust solution, as it is less affected by external disturbances. This study introduces an adaptive network-based fuzzy inference system (ANFIS) for AOA/AOS data fusion, which compensates for inaccuracies in sensor measurements by integrating UKF-estimated AOA and AOS values. Flight test results demonstrate that the proposed ANFIS model achieves an average relative error of less than 15%, with the average relative errors being 10 . 26% for AOA and 12 . 77% for AOS. This fusion approach significantly enhances the accuracy of AOA and AOS measurements, providing a valuable reference for small-sized fixed-wing UAVs.

• Publication year

  2025

• Venue

  International Conference on Enterprise Information Systems

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.5220/0013431700003929
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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