Funnel Control of Uncertain Flexible Air-breathing Hypersonic Vehicles

Abstract

A funnel control approach, guaranteeing output behavioral performance, is studied for flexible air-breathing hypersonic vehicle. A performance function without requiring an accurate initial error is designed for the sake of guaranteeing tracking errors with preseted transient performance. Neural approximation approach is applied to reject unknown vehicle dynamics. Moreover, improved regulation laws are developed for neural networks applying minimal-learning-parameter scheme, which reduces the computational costs and ensures neural approximation precision. The stability of closed-loop control systems is proved via Lyapunov synthesis. Finally, the presented simulation results show the tracking performance of the addressed approach.