Flight Control of Waverider Vehicles with Fragility-avoidance Prescribed Performance

Abstract

This article proposes a prescribed performance control (PPC) methodology namely fragility-avoidance PPC for Waverider Vehicles (WVs) with sudden disturbances based on fuzzy neural approximation. We raise the fragile problem associated with the existing PPC, and to remedy this defect, we construct a flexible prescribed funnel that is able to sense the error fluctuation caused by sudden disturbances and moreover tackle the fragile problem by automatically adjusting prescribed boundaries. Then, a simplified fuzzy neural approximation framework is presented to reject the unknown non-affine dynamics of WVs, while avoiding the algebraic loop problem. The stability of closed-loop system is proved via Lyapunov method, and finally, the effectiveness and superiority of the addressed method are verified by compared simulations.