Design of a robust flight control law based on dynamic surface control and neural network

A design method for flight controllers using the RBF neural network and dynamic surface control is proposed.The RBF neural network is used to approximate the aerodynamic force and moment coefficients.The problem of expose in terms of traditional back stepping design,which is caused by repeated derivatives of certain nonlinear functions such as virtual control,is overcome by the dynamic surface control method.Then,a robust term is introduced to compensate the external disturbance and the approximation error,which can improve the robustness,and guarantee much higher tracking accuracy.All signals in the close loop are guaranteed to be semi-globally uniformly ultimately boundedness and the output tracking error is proved to converge to a small neighborhood around zero by appropriately choosing design parameters.Simulation results for aircraft pitch movement demonstrate the effectiveness of the proposed method.