考虑执行器故障的高超声速飞行器滚动 学习容错控制

A receding learning fault-tolerant control (RLFTC) method is developed to address trajectory tracking challenges in longitudinal flight of air-breathing hypersonic vehicles (ABHV) when scramjets and steering gears experience faults. Linear models are employed to decouple actuator faults from system states. Forward model prediction observers estimate system states within the receding time domain. Adaptive iterative learning control (ILC) achieves precise estimation of actuator faults and external disturbances with minimal computational load. Model predictive controllers (MPC) are designed using fault estimation results, enabling rapid and stable longitudinal maneuvering even under actuator failures and external disturbances. Stability of the control scheme is rigorously analyzed based on Lyapunov stability theory. Numerical simulations involving external disturbances and sudden actuator faults demonstrate the effectiveness of the proposed method, confirming its practical applicability for reliable hypersonic flight operations.