System identification for adaptive and reconfigurable control

Abstract

In this work, an efficient static system identification method is developed that incorporates prior information. The prior information from flight mechanics enhances the performance of the identification algorithm, yielding more accurate parameter estimates. The proposed static system identification approach is superior to dynamic system identification for on-line identification of rapidly varying plant parameters, making it suitable for adaptive and reconfigurable control. The effectiveness of the static system identification scheme is illustrated in an example problem. A derivative F-16 aircraft control surface (elevator) failure is simulated and the abruptly changing pitch channel control system's stability and control derivatives are successfully identified on-line in the presence of unmodeled dynamics, process noise (clear air turbulence), and realistic measurement noise.