Comparison of linear and nonlinear aerodynamic parameter estimation approaches for an unmanned aerial vehicle using unscented Kalman filter

Aerodynamic parameter estimation provides an effective way for aerospace system modeling using measured data from flight tests, especially for the purpose of developing elaborate simulation environments and designing control systems of unmanned aerial vehicle (UAV) with short design cycles and reduced cost. However, parameter identification of airplane dynamics by nonlinear models is complicated because of the noisy and biased sensor measurements. Using linear models for system identification is an alternative way if the fidelity can be guaranteed, as control design procedures are better established in linear systems. This paper considers the application and comparison of linear as well as nonlinear aerodynamic parameter estimation approaches of an UAV using unscented Kalman filter (UKF). It also highlights the degree of deterioration of the linear model in the UKF identification process. The results show that both the linear and nonlinear methodologies can accurately estimate the control system design. Furthermore, considering loss of accuracy to be negligible, the linear model can be employed for control design of the UAV as presented here.