This paper proposes an adaptive fault-tolerant cooperative control (FTCC) scheme for networked unmanned aerial vehicles (UAVs) in the presence of actuator faults and wind effects by artfully introducing fractional order calculus, proportional–integral–derivative (PID), and recurrent neural networks. Fractional order sliding-mode surface and PID-type error mapping are first utilized to transform the synchronization tracking errors of all UAVs into a new set of errors. Then, based on these newly constructed errors, an FTCC scheme is developed to synchronously track their references. Moreover, Butterworth low-pass filter (BLF) and recurrent neural network (RNN) learning strategies are assimilated to handle the unknown terms induced by the actuator faults and wind effects. Finally, theoretical analysis and comparative hardware-in-the-loop experimental demonstrations have shown the effectiveness of the proposed control scheme.

本文通过巧妙地引入分数阶微积分、比例积分微分 (PID) 和递归神经网络，提出了一种在存在执行器故障和风效应的情况下用于联网无人机 (UAV) 的自适应容错协作控制 (FTCC) 方案。网络。首先利用分数阶滑模面和PID型误差映射将所有无人机的同步跟踪误差转化为一组新的误差。然后，基于这些新构造的错误，开发了一种 FTCC 方案来同步跟踪它们的引用。此外，巴特沃斯低通滤波器 (BLF) 和循环神经网络 (RNN) 学习策略被同化以处理由执行器故障和风效应引起的未知项。最后，