Resilient Prescribed Performance Control for Hypersonic Morphing Flight Vehicle with Measurement Uncertainty

This paper proposes a resilient prescribed performance control scheme for a hypersonic morphing flight vehicle (HMFV) in the presence of measurement uncertainties, external disturbances, and parametric uncertainties. The primary objective is to design a resilient control scheme for the HMFV system, such that the state variables can track the reference signals with inaccurate measurements and all tracking errors remain within the user-defined boundaries. First, for the measurement uncertainties, an adaptive neural network-based observer is designed to reconstruct the precise measurements, in which both state measurement uncertainty and output measurement uncertainty are considered. Subsequently, based on the reconstructed information, a prescribed performance controller (PPC) with self-adjusting performance boundaries is developed. Unlike existing fixed boundary PPC methods, the proposed framework dynamically adapts performance boundaries when the HMFV undergoes structural reconfiguration or encounters sudden disturbances, overcoming the limitations of rigid constraints in conventional approaches. Finally, numerical simulations are carried out to validate the effectiveness of the proposed method, and the results are compared against those obtained using conventional methods.