Flight Phase Classification for Small Unmanned Aerial Vehicles

Abstract

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.

A Mathematical Model for Identifying Military Training Flights

“…Flight analysis is a prevalent topic in world literature, with a particular focus on flight safety, constituting the primary theme of most publications. Authors delve into the analysis of human factor (Kelly & Efthymiou, 2019;Amalberti & Wioland, 2020), the potential for detecting flight anomalies (Sheridan et al, 2020;Wei et al, 2023;Kosacki & Tomczyk, 2022), diagnosing faults (Su et al, 2023), and identifying defects in both manned and unmanned aircraft (Chen et al, 2020;Czyż et al, 2023;Leško et al, 2023). Many publications also address ecological concerns and the minimization of negative impacts on the natural environment, even during the aircraft design stage (Parolin et al, 2021).…”