Adaptive Tracking Control for Air-breathing Hypersonic Vehicles with Attack Angle Constraints

The design of the attack angle constrained control for hypersonic vehicles is important to ensure the scramjet engine run normally in practice, and is not taken into account in many existing results. In this paper, an adaptive neural control scheme is proposed for the longitudinal model of a class of hypersonic vehicles. Based on the backstepping technique and barrier Lyapunov functions, the attack angle can be confined in a predefined set. Besides, with the aid of the minimal learning parameter technique, there is only one parameter needs to be estimated at each design step. The proposed scheme is able to guarantee that all closed-loop signals are bounded and all tracking errors converge to some small residue sets. Simulation studies are presented to illustrate the effectiveness of the proposed control scheme.