Online identification based strong adaptive control of hypersonic morphing vehicles

This paper investigates the robust adaptive control problem for high-speed morphing aircraft under model uncertainties, external disturbances, and non-minimum phase characteristics. First, a dynamic model incorporating uncertainties is established and subsequently decomposed into velocity and attitude subsystems based on system characteristics. A modified forgetting factor-based online parameter identification method is designed to estimate aerodynamic parameters in real-time, reducing reliance on prior model knowledge while providing real-time mode evaluation information for controller design. Subsequently, a game-enhanced neural network observer is proposed to handle composite disturbances, including identification errors, morphing uncertainties, and external disturbances, ensuring finite-time convergence of disturbance estimation errors to zero. By developing a morphing-redefinition strategy to reconfigure system outputs, specifically defined reference commands are generated for morphing configurations to avoid unstable internal dynamics caused by the coupling between elevators and lift. Finally, system stability is rigorously analyzed using Lyapunov theory, with simulation results validating the effectiveness of the proposed methodology.