Adaptive Performance-Constrained Synchronization Control for Hypersonic Flight Vehicle Swarms: A Novel Finite-Time Convergence Protocol Methodology

This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles (HFVs), which are subject to actuator faults and full-state constraints. The proposed method is based on advanced Lyapunov finite-time stability theory and a sophisticated backstepping design scheme. The longitudinal model of HFV is converted into velocity and altitude subsystems through functional decomposition. Our method presents three significant contributions over the existing state-of-the-art approaches: (a) ensuring finite-time convergence of HFVs systems by guaranteeing that the setting time is lower bounded by a positive constant that is related to the initial states; (b) utilizing a tan-type Barrier Lyapunov function (BLF) to ensure that the synchronization tracking errors of velocity, altitude, flight path angle, angle of attack, and pitch angle rate are maintained within certain performance bounds; and (c) designing a neural adaptive control algorithm and adaptive parameter laws by combining the backstepping design technique and radial basis function neural networks (RBFNNs) to handle unknown actuator faults and modeling uncertainties. Finally, comparative simulations are conducted to validate the efficacy of the proposed scheme.