This paper introduces an innovative adaptive event-triggered fault-tolerant attitude control framework designed for a flying-wing unmanned aerial vehicle (UAV) operating under constraints such as limited embedded resources, unknown actuator failures, system uncertainties, and external disturbances. The proposed scheme incorporates several noteworthy features: (i) Implementation of a relative threshold event-triggered mechanism to efficiently alleviate communication pressures and computational burdens inherent in the attitude control system. (ii) Utilization of a radial basis function neural network to approximate lumped disturbances, reducing dependence on prior knowledge. (iii) Adaptive compensation for sampling errors and actuator faults by employing the Nussbaum gain. (iv) Integration of a smooth function to address singularity issues and prevent Zeno behavior. Furthermore, Lyapunov analysis validates that all signals within the closed-loop system remain bounded and converge within a predetermined time frame. Comparative numerical simulations underscore the effectiveness and superiority of the proposed control approach.

中文翻译：

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基于 Nussbaum 函数的飞翼无人机事件触发定时容错姿态控制


本文介绍了一种创新的自适应事件触发容错姿态控制框架，专为在嵌入式资源有限、执行器故障未知、系统不确定性和外部干扰等约束下运行的飞翼无人机（UAV）而设计。所提出的方案包含几个值得注意的特征：（i）实现相对阈值事件触发机制，以有效减轻姿态控制系统固有的通信压力和计算负担。 (ii)利用径向基函数神经网络来近似集总扰动，减少对先验知识的依赖。 (iii)通过采用努斯鲍姆增益对采样误差和执行器故障进行自适应补偿。 (iv) 集成平滑函数以解决奇点问题并防止 Zeno 行为。此外，李亚普诺夫分析验证了闭环系统内的所有信号都保持有界并在预定时间范围内收敛。比较数值模拟强调了所提出的控制方法的有效性和优越性。