Guaranteeing preselected tracking quality for air-breathing hypersonic non-affine models with an unknown control direction via concise neural control

Abstract A simplified neural controller is addressed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) with a completely unknown control direction by utilizing the prescribed performance control scheme. Unlike the existing literatures, the exploited methodology does not require an affine AHV model or any prior information about the sign of control gains. Moreover, the proposed strategy can provide preselected bounds on the transient and steady performance of velocity and altitude tracking errors. The altitude dynamics is converted into a pure feedback formulation with an unknown control direction, based on which, a novel adaptive neural controller that is quite simpler than the ones derived from back-stepping designs is achieved. For the problem of the unknown control direction, a Nussbaum-type function is introduced to handle it. By employing the minimal-learning parameter (MLP) technique to regulate the norm instead of the elements of the ideal weight vector, only one learning parameter is required for neural approximation. Thus, a low computational burden design is obtained. Finally, simulations are performed to verify the presented control approach.