针对具有执行器故障的四旋翼无人机姿态控制问题，提出了一种基于径向基函数（radial basis function, RBF）神经网络、事件触发机制和迟滞量化器的自适应规定时间预设性能反演容错控制策略。首先，构造四旋翼无人机系统的动力学模型，并结合执行器故障模型重构姿态模型。其次，通过设计一类时变函数，对反演控制所需误差变量进行变换，使其满足规定时间内达到预设性能指标的要求。再次，利用RBF神经网络的非线性函数逼近能力对虚拟控制律的导数以及具有未知参数的执行器故障进行估计。最后，为了降低执行器的更新频率，结合事件触发技术和迟滞量化器设计自适应反演控制输入。通过Lyapunov稳定性理论证明了闭环系统的稳定性，并由MATLAB软件仿真验证了提出算法的有效性。实验结果表明所设计的事件触发量化控制器同仅利用事件触发技术设计的控制器相比具有更低的更新频率。

An adaptive predefined-time prescribed performance backstepping fault-tolerant control strategy is presented based on radial basis function (RBF) neural networks, event-trigger mechanism and hysteresis quantizer for the attitude control problem of quadrotor unmanned aerial vehicle (UAV) with actuator faults. Firstly, the dynamic model of the quadrotor UAV system was constructed, and the attitude model was reconstructed by incorporating the actuator fault model. Secondly, by designing a class of time-varying functions, the error variables required for backstepping control were transformed. Thirdly, the nonlinear function approximation capability of RBF neural networks was utilized to estimate derivatives of virtual control laws and the actuator fault with unknown parameters. Finally, to reduce the update frequency of the actuator, a combination of event-trigger mechanism and hysteresis quantizer was used to design the control input. Stability of the closed-loop system was demonstrated through Lyapunov stability theory. The effectiveness of the proposed algorithm was verified through MATLAB. It is concluded that the designed event-triggered quantized controllers have a lower update frequency compared to controllers designed using only event-triggered techniques.