High-g maneuver dynamic surface control of fighter plane under input saturation

An adaptive neural network dynamic surface control method is proposed to resolve the input saturation problem of aircraft high- g maneuver flight. The Radial Basis Function (RBF) neural networks are utilized to approximate the unknown uncertain parts of aircraft model. The hyperbolic tangent function is used to handle the system input saturation problem. A new error is defined by the difference between saturated actual control input and desired control input, and a high- g maneuver flight control law is designed by combining this error, and the robust term is constructed to offset the influence of approximation error of neural network, external interference and modeling errors. The dynamic surface control technique is used to avoid the complex derivative operation of the virtual controller and reduce computation amount. It is proved from Lyapunov stability theorem that all the signals in the closed-loop control system are bounded, and the attitude angle tracking error can converge to an arbitrarily small neighborhood around zero by choosing the appropriate design parameters. Simulation results demonstrate the good robustness and stability of the proposed method.