Nonlinear System Identification of Fixed Wing UAV Aerodynamics

We apply the NARMAX approach for nonlinear system identification in order to evaluate it for identifying the aero- dynamic 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 NAR- MAX 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. Re- sults from the real world data set were found inconclusive but promising.