LAUSR

Abstract This paper investigates a new prescribed performance control (PPC) methodology for the longitudinal dynamic model of an air-breathing hypersonic vehicle via neural approximation. To release the restriction on traditional PPC that the initial tracking errors have to be known in advance for control design, a novel performance function is exploited. Moreover, the devised controller is capable of guaranteeing prescribed performance on the velocity and altitude tracking errors. Neural networks (NNs) are employed to approximate the unknown vehicle dynamics and a minimal-learning parameter scheme is utilized to update the norm of NN's weight vector. Hence, a low computational burden design is achieved without using back-stepping. The semi-globally uniform boundedness of all the closed-loop signals is insured by Lyapunov synthesis. Finally, simulation results are presented to validate the efficacy of the proposed control approach.