Adaptive neural control design for hypersonic aircraft using time scale separation

Abstract

This paper presents the design of a neural adaptive flight control systems for the longitudinal dynamics of hypersonic vehicle. By considering the coupling between thrust and pitch moment, the proposed control strategy is derived from the solutions of a series of fast dynamical equations, which are designed by the combination of back-stepping control and time scale separation. The RBF neural networks are employed to approximate the unknown dynamics. Simulation results are included to show the effectiveness of the neural adaptive control method.

Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

“…They may represent more cost-efficient and reliable access to space routine and are especially suitable for prompt global response, as well as offering worldwide air superiority because of the high speed and endurance [1][2][3][4][5]. In this paper a nonlinear generic model of HFVs is adopted, which has been widely used by various researchers [6][7][8]. The dynamics of HFVs are highly nonlinear with strong couplings between the propulsive and aerodynamic effects.…”