FSE-RBFNNs-Based Adaptive Tracking Control of Hypersonic Flight Vehicles with Uncertain Periodic Time-Varying Disturbances

This work for the first time develops a neuro-adaptive control strategy for an extended class of longitudinal dynamics of hypersonic flight vehicles (HFVs). To handle with the design difficulty that the uncertain time-varying disturbances appear implicitly in HFVs dynamics, a new function approximator is designed by incorporating the fourier series expansion (FSE) into the radial basis function neural networks (RBFNNs). An integral term and a linear term are, respectively, constructed to speed up the convergence rate and compensate for the negative effects caused by approximation errors. It is rigorously proved that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Simulation results verify the effectiveness of the proposed control methodology.