Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Blog Article

In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) directly using nonaffine models instead of affine ones.For the velocity dynamics, an adaptive neural controller is devised based MOISTURIZER GEL on a minimal-learning parameter (MLP) technique for the sake of decreasing computational loads.The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping.The special contributions are that the control architecture is concise and the computational cost is low.Moreover, the exploited controller Triple-barrel Curling Iron possesses good practicability since there is no need for affine models.

The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory.Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.