Longitudinal Motion System Identification of a Fixed-Wing Unmanned Aerial Vehicle Using Limited Unplanned Flight Data

Abstract

Acquiring knowledge of aircraft flight dynamics is crucial for simulation, control, mission performance and safety assurance analysis. In the fast-paced UAV market, long flight testing campaigns are hard to achieve, leaving limited controlled flight data and a significant amount of unplanned flight data. This work delves into the application of system identification techniques on unplanned flight data when faced with a shortage of dedicated flight test data. Based on a medium-sized, fixed-wing UAV, it focuses on the system identification of longitudinal dynamics using structural routine flight test data of pitch down and pitch up manoeuvres with no specific guidelines for the control inputs given. The proposed solution uses first- and second-order parameter-based models to build a non-linear dynamic model which, using a least square error optimisation algorithm in a time domain formulation, has its parameters tuned to converge the model behaviour with the real aircraft dynamics. The optimisation uses a combination of pitch, altitude, airspeed and pitch rate responses as a measure of model accuracy. Very significant improvements regarding the UAV model response are found when trimmed flight manoeuvres are used, resulting in proper estimation of important aerodynamic and control derivatives. Pitching moment and control derivatives are shown to be the crucial parameters. However, difficulties in estimation are shown for untrimmed flight manoeuvres. Better results were obtained when using multiple manoeuvres simultaneously in the optimisation error metric, as opposed to single manoeuvres that led to system bias. The proposed system identification procedure can be applied to any fixed-wing UAV without the need for specific flight testing campaigns.