Fault-tolerant Disturbance Rejection Pose Tracking Control for Quadrotor UAV Based on RBF-SMO

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 quadrotor 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 unknowndisturbances 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.