干扰与传感器故障下固定翼无人机抗饱和控制OA

The performance of Fixed-Wing Unmanned Aerial Vehicles(FWUAVs)in complex dynamic environments is negatively affected by external time-varying disturbances,sensor faults,and input/output nonlinear constraints.The sensor faults and output constraints can lead to inaccurate measurement of state information,while input saturation can limit the output capacity of actuators.Unfavorable external time-varying disturbances can also reduce flight control performance.The combined effect of these factors may cause the FWUAVs to lose control.To address these issues,this paper proposes a neural network adaptive control method based on the state observer,the fault observer,the dis-turbance observer,and an auxiliary system.Firstly,a FWUAV attitude dynamics model considering the combined ef-fects of sensor fault,external disturbance,output and input saturation is established.Secondly,the radial basis func-tion neural networks are integrated to design the state observer,the fault observer,and the disturbance observer to estimate unknown states,actuator fault,and external disturbances,respectively.The outputs of these three observ-ers,the first-order filter and the auxiliary system state variables are combined for the controller design.Meanwhile,the Lyapunov stability theory is employed to prove that all signals in the closed-loop system are ultimately uniformly bounded.Finally,simulation results demonstrate that the proposed method can ensure the stable flight of FWUAVs under the combined effects of external time-varying disturbances,sensor faults,and input/output saturation.