Guaranteeing prescribed performance for air-breathing hypersonic vehicles via an adaptive non-affine tracking controller

This paper investigates a prescribed performance control strategy for air-breathing hypersonic vehicles (AHVs) based on neural approximation. Different from the existing studies, the explored controllers are derived from non-affine models instead of affine ones. For the velocity dynamics, an adaptive neural controller containing only one neural network (NN) is addressed via prescribed performance control. Specially, the altitude dynamics is transformed into a pure feedback non-affine model instead of a strict feedback one. Then a novel adaptive neural controller is exploited without using back-stepping. Also, only one NN is utilized to approximate the lumped unknown nonlinearity of the altitude subsystem. By the merit of the minimal-learning parameter (MLP) scheme, only two learning parameters are required for neural approximation. The highlights are that the proposed control methodology possesses concise control structure and a low computational cost and moreover it can guarantee the tracking errors with prescribed performance. Finally, simulation results for an AHV model are provided to demonstrate the efficacy of the proposed control approach.