Adaptive Kalman Filter Tracking for Instantaneous Aircraft Flutter Monitoring

Abstract

The aeroelastic behaviour of aircraft is parameter variant. Changing flight conditions, such as e.g. flight velocity and altitude may change the vibration damping. When the vibration damping becomes zero or negative, self-excitation of the vibration occurs, called flutter. Modal parameter identification can be applied to extract eigenfrequencies and damping ratios based on e.g. acceleration data. In order to avoid flutter, modal parameters can be identified in flight testing of a new aircraft type close to real-time using optimized algorithms. Real-time identification of modal parameters has significant uncertainties, especially with respect to damping ratios. Those uncertainties cannot be calculated, but qualitatively estimated. In this study, a Kalman filter tracking is applied to reduce the uncertainties of modal parameter monitoring of aircraft. Since the process noise of such a system is impossible to foresee and is expected to change throughout a flight, the process noise is adapted with respect to the innovation and changing flight conditions. This contextaware adaptive Kalman filter is tested on data from a simulated aeroelastic model as well as on real flight test data of a smallscale fixed-wing UAV. The results show significant reduction of the identification uncertainties for both simulated and real data.