Reconfigurable flight control for a tailless advanced fighter aircraft

Abstract

This paper presents a reconfigurable flight control approach based on a dynamic inversion control law in an explicit model following framework. An on-line neural network is used to adaptively regulate the error in the plant inversion, which may be due to modeling uncertainties, failures, and damage. On-line control allocation is used to generate individual control effector commands which yield the desired rotational accelerations while optimizing performance objectives such as maneuver load alleviation and radar signature. On-line system identification is used to estimate the control derivatives used by the control allocation algorithms. Filters on the pilot inputs are used to augment the control effector commands with signals in the kernel of the control distribution matrix. This allows identification of parameters which are colinear due to the control ganging resulting from the control allocation algorithms. This reconfigurable control law is demonstrated via an application to a tailless advanced fighter aircraft. The results presented in this paper are based on work performed by the Boeing team1^ on the Air Force sponsored RESTORE program*.