Multi-model switching control of hypersonic vehicle with variable scramjet inlet based on adaptive neural network

The variable-geometry scramjet inlet can improve the aerodynamic performance of hypersonic vehicle by moving the movable lip to capture more airflow, which causes the uncertainties of model structures and parameters in the meanwhile. This paper presents a multi-model switching control system to tackle the strong nonlinearity and interaction characteristics. According to the working conditions, we build several nonlinear aerodynamic models with different lengths of the movable lip. For each model, a specific neural network controller has been adopted. By constructing the common Lyapunov function, it is proved that all signals of the closed-looped system are uniformly ultimatedly bounded by the continuous controller. Numerical simulations are presented to verify the feasibility of the proposed control approach.