Comparison of Time and Frequency Domain Identification of a Fixed-Wing UAV

he main advantage of a UAV over manned aircraft lies in remote control or autonomous flight. They are low cost and presents lower risk to the operator and environment. Remote control and fully autonomous flight are difficult to get used to for pilots since controlling aircraft without sensing aircraft states is very challenging. On the other hand autonomous control requires complex control and error handling algorithms to ensure that the vehicle is safely recovered in the event of a system failure. Moreover, UAVs are very appropriate to use for flight control system design studies due to their low cost and low risk of damage or harm. Therefore, an accurate mathematical model of UAV dynamic is necessary for pilot training and control system design. However, obtaining an accurate model of UAV is not easy. Aerodynamic modeling techniques such as using CFD or wind tunnel testing can be too costly for a UAV project. These models cannot accurately match the actual aircraft due to accumulated uncertainties and modeling simplifications. Therefore practical ways of identifying the flight dynamic properties of small fixed wing UAVs accurately are important. In this paper, an overview of the on-going research on bare airframe identification of a UAV at Turkish Aerospace Industries, Inc. (TAI) is presented. Two different approaches are studied to identify UAV dynamics using flight test data: time domain and frequency domain identifications. The flight test data used in this study are obtained from the remotely piloted flight of the UAV with autopilot disengaged. The frequency domain identification is based on the methods of Ref 1 using CIFER and time domain identification is obtained by using methods of Ref 2 and its software FVSysID.