Neural discrete back-stepping control of hypersonic flight vehicle with equivalent prediction model

Abstract

This paper describes the back-stepping controller design in discrete time for the longitudinal dynamics of a generic hypersonic flight vehicle with neural networks. For the altitude subsystem, to avoid the causality contradiction, the original system is formulated into a new equivalent prediction model with strict-feedback form from which the virtual control is designed with error feedback and neural approximation via back-stepping. For stability analysis, Lyapunov function is chosen with regard to the novel formulation of new strict-feedback system. Under the proposed controller, the semiglobal uniform ultimate boundedness stability is guaranteed. The step response is presented in the simulation studies to show the effectiveness of the proposed control approach.