Adaptive backstepping control for flight path angle based on high gain observer

An adaptive neural network high gain observer was designed to solve the problem that the states of the flight control system were not absolutely measurable. The adaptive items based on the radial basis function (RBF) network were introduced into the general high gain observer on purpose of the on-line estimation of the modeling errors and the external disturbances. With the combination of the high gain observer and the backstepping control based on the dynamic surface, an adaptive neural network backstepping control approach was proposed. The traditional problem of explosion of calculation in the backstepping control was avoided by the introduction of the first-order filter. The adaptive output feedback controller and the adaptive laws of the RBF network weight vectors were obtained on the basis of the Lyapunov stability theory. It is proved that the closed-loop system is semi-globally and uniformly bounded. The simulation results of the flight path angle control system show that the flight path angle can track the command signal without the effect of the modeling errors and the external disturbances, the convergence of the observer is concluded and the robustness of the control system is verified.