Kalman-filter based online system identification of fixed-wing aircraft in upset condition

Abstract

Online system identification became an integral part of the design process for aerodynamic parameter estimation with the technological progress. This paper presents two Kalman-filter based online system identification (SID) techniques for estimating aerodynamic parameters of fixed-wing aircraft in upset condition like stall. Unlike existing SID ones, the proposed methods first include aerodynamic characteristics directly in the aircraft dynamics, i.e. variation of aerodynamic derivatives or flow separation point, associated with the upset condition. Then, the conventional or unscented Kalman filter is applied in real time to obtain optimal estimates of the aerodynamic parameters under consideration. The proposed methods are tested with real flight data sets of several aircraft to demonstrate their effectiveness and superiority to a recently proposed method.