Bu Xiangwei, He Guangjun, Wang Ke
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
ISA Trans. 2018 Apr;75:88-100. doi: 10.1016/j.isatra.2018.02.010. Epub 2018 Feb 17.
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.
