Online system identification of mini cropped delta UAVs using flight test methods

Abstract

The current manuscript presents the longitudinal and lateral directional online parameter estimation of two unmanned aerial vehicles (UAVs) using sequential Least Squares formulation in frequency domain. The two fixed wing UAVs share a similar cropped delta planform and differ in their cross sectional geometries, one with a rectangular and the other being a reflex airfoil cross sections respectively. Recursive Fourier Transform algorithm has been used to convert the flight data in time domain to frequency domain which is measured by means of a dedicated on-board data acquisition system capable of on-board logging and telemetry to ground station. The combination of Sequential Least Squares with Recursive Fourier Transform (SLS-RFT) in frequency domain can be used to carry out online parameter estimation. An attempt has been made to check the applicability of the current method to estimate parameters from the generated flight data of the two UAVs using both conventional as well as random control inputs. Results showed that the parameters estimated, using SLS-RFT, from the linear flight data are consistent and in close agreement with the obtained parameters from full scale wind tunnel testing of UAVs. It was also observed that the estimates from the manoeuvres with multistep control inputs converged faster compared to the parameters obtained from the manoeuvres with slow varying control surface deflections. The time varying linear aerodynamic parametric model of SLS-RFT was able to capture the dynamics of the flights with nonlinear aerodynamics. Certain limitations of the current online system identification method were also observed with estimating parameters from the flight data of UAVs performing near stall manoeuvres. The estimated parameters using SLS-RFT are also compared with the results obtained from batch methods namely classical Maximum Likelihood (ML) and neural based Neural-Gauss-Newton (NGN) methods. (C) 2018 Elsevier Masson SAS. All rights reserved.