Tracking control of air-breathing hypersonic vehicles with non-affine dynamics via improved neural back-stepping design. (April 2018) - Bu, Xiangwei; He, Guangjun; Wang, Ke
Abstract: This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy. Highlights: The non-affine controller presents good reliability and practicality. The adaptive parameters and computational costs are reduced. Improved back-stepping controllers are addressed without virtual control laws.