Air-Breathing Hypersonic Vehicles Funnel Control Using Neural Approximation of Non-affine Dynamics

Abstract

This paper presents a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs). We propose a new funnel control to force velocity and altitude tracking errors to fall within bounded funnels, while the desired transient performance and steady-state performance are ensured for both tracking errors. To handle the non-affine dynamics, a simplified neural controller is addressed for a velocity subsystem based on implicit function theorem, and a new back-stepping control without virtual control laws is exploited for the altitude subsystem via a model transformation combined with low-pass-filter approach. Neural approximations and regulation laws for guaranteeing approximation performance are employed to reject system unknown dynamics. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov synthesis. Finally, the tracking performance of the proposed control approach is verified by simulation results.