四旋翼无人机RBF-SMO容错抗扰位姿跟踪控制OA北大核心

The quadrotor UAV is easily-influenced to actuator failure faults and unknown environmental disturbances during the practical flight,which severely affects the safety of the flight.Therefore,a novel composite fault-tolerant pose tracking control strategy is proposed for the dynamics of quadro-tor UAV with the time-varying actuator faults and the time-varying external disturbances.In the position loop,a PD(Proportional-Derivative)control strategy is designed to achieve the position tracking control for quadrotor UAV.In the attitude loop,by combining an adaptive neural network with a fixed-time sliding mode observer(SMO),a composite fault-tolerant pose tracking control strategy is designed.A radial basis function(RBF)neural network is used to compensate for time-varying actuator faults in real time.Meanwhile,to overcome the impact of external disturbances on UAV flight performance,an SMO is designed to compensate for time-varying external unknown disturbances in real time and to reduce system chattering.Finally,the stability of the closed-loop system is proven based on Lyapunov analysis theory.The numerical simulation results demonstrate that the proposed composite fault-tolerant pose tracking control strategy exhibits faster convergence speed,higher pose tracking accuracy,and stronger fault-tolerant control performance compared to the traditional PID(Proportional-Integral-Derivative)control strategy.