Neural network based adaptive dynamic surface control for flight path angle

A neural network based adaptive dynamic surface control is proposed for the aircraft longitudinal flight path angle. The dynamic surface control method eliminates the problem of “explosion of complexity” existing in traditional backstepping approach with the introduction of low pass filters. Radial basis function (RBF) neural networks are used to approximate the unknown nonlinearities of the model online. Adaptive laws are designed to estimate the weight values of the neural networks and unknown parameters. From Lyapunov stability analysis, it is shown that the control strategy can guarantee the semi-global practical tracking and arbitrarily small tracking error by adjusting the controller parameters. Simulation results are presented to validate the good tracking performance and strong adaptability of the control system.