Adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles with parametric uncertainties

Control system is significant for making flight safety. In this study, a novel adaptive neural back-stepping controller is exploited for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle. A combined neural network approach and back-stepping scheme is utilized for developing an output-feedback controller that provides robust tracking of the velocity and altitude commands. For each subsystem, only one neural network is employed to approximate the lumped system uncertainty by updating its weight vector adaptively while the problem of possible control singularity is eliminated. The uniformly ultimately boundedness is guaranteed for the closed-loop control system by means of Lyapunov stability theory. The main contribution is that the design complexity is reduced and less neural networks are required. Finally, simulation results illustrate that the proposed control strategy achieves satisfying tracking performance in spite of flexible effects and system uncertainties.