عنوان مقاله [English]
A nonlinear adaptive flight control system is proposed using a backstepping and neural network controller. The backstepping
controller is used to stabilize all state variables simultaneously without the two-timescale assumption that separates the fast dynamics, involving the angular rates of the aircraft, from the slow dynamics, which includes angle of attack, sideslip angle
and bank angle. It is assumed that the aerodynamic coefficients include uncertainty and an adaptive controller, based on neural
networks, is used to compensate for the effect of the aerodynamic modeling error. Neural networks are used to represent the nonlinear inverse transformation needed for feedback linearization. Neural networks capable of on-line learning are required to compensate for inversion error, which may arise from imperfect modeling, approximate inversion, or sudden change in aircraft dynamics. A stable weights adjustment rule for on-line training to the network is derived. Under mild assumptions on the nonlinearities representing the inversion error, the adaptation algorithm ensures that all of the signals in the loop are uniformly bounded and that the weights of the neural network tend to some constant values. It is shown by the Lyapunov stability theorem that tracking errors and the weights of neural networks exponentially converge to a compact set. Finally, nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model are presented to demonstrate the effectiveness of the proposed control laws.