Nonlinear System Identification of Fixed Wing UAV Aerodynamics

Tor-Aleksander Johansen, Annette Fossli Brustad, Tom Stian Andersen, and Raymond Kristiansen

Keywords

System Identification, NARMAX, FROLS, Linear Regression

Abstract

We apply the NARMAX approach for nonlinear system identification in order to evaluate it for identifying the aerodynamic model of a fixed-wing Unmanned Aerial Vehicle (UAV). A nonlinear observer is implemented to estimate vehicle states along with wind data such as angle of attack and sideslip angle. Simulation results show that the NARMAX approach has potential to estimate the correct model even when measurements is affected by noise, and in all cases produces a better model than the one provided by the Least Squares solution using a known model structure. Results from the real world data set were found inconclusive but promising.

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