Flight phase classification for small unmanned aerial vehicles

    Jakub Leško   Affiliation
    ; Rudolf Andoga   Affiliation
    ; Róbert Bréda Affiliation
    ; Miriam Hlinková   Affiliation
    ; Ladislav Fözö   Affiliation


This article describes research on the classification of flight phases using a fuzzy inference system and an artificial neural network. The aim of the research was to identify a small set of input parameters that would ensure correct flight phase classification using a simple classifier, meaning a neural network with a low number of neurons and a fuzzy inference system with a small rule base. This was done to ensure that the created classifier could be implemented in control units with limited computational power in small affordable UAVs. The functionality of the designed system was validated by several experimental flights using a small fixed-wing UAV. To evaluate the validity of the proposed system, a set of special maneuvers was performed during test flights. It was found that even a simple feedforward artificial neural network could classify basic flight phases with very high accuracy and a limited set of three input parameters.

Keyword : flight phase, classification, fuzzy logic, artificial neural network, unmanned aerial vehicles

How to Cite
Leško, J., Andoga, R., Bréda, R., Hlinková, M., & Fözö, L. (2023). Flight phase classification for small unmanned aerial vehicles. Aviation, 27(2), 75–85.
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May 5, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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