Aircraft control augmentation system Design Using Dynamic Inversion and Neural Network

This paper presents a control augmentation system (CAS) based on the dynamic inversion (DI) and neural networks for a highly maneuverable aircraft. A neural flight control system is used to provide adaptive flight control, without requiring gain-scheduling or system identification. Neural networks on-line is used to compensate for inversion error which arise from imperfect modeling, approximate inversion, or sudden change in aircraft dynamics. A stable weights adjustment rule for the on-line neural network is derived by the Lyapunov stability theorem. Finally, nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model are presented to demonstrate the effectiveness of the proposed CAS.