SYSTEM-IDENTIFICATION FOR ADAPTIVE AND RECONFIGURABLE CONTROL

In this work, an efficient static system identification method is deve loped that incorporates prior information. The prior information from flight mechanics enhances the performance of the identification algori thm, yielding more accurate parameter estimates. The proposed static s ystem identification approach is superior to dynamic system identifica tion for on-line identification of rapidly varying plant parameters, m aking it suitable for adaptive and reconfigurable control. The effecti veness 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 identifie d on-line in the presence of unmodeled dynamics, process noise (clear air turbulence), and realistic measurement noise.