This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

中文翻译：

---

智能城市观测中通信范围有限的无人飞艇针对输入饱和的分布式自适应容错时变编队控制


本文研究了针对有限通信范围和输入饱和的多个无人飞艇（UA）的分布式容错时变编队控制问题，以实现智慧城市的安全观测。为了解决通信范围有限和偏差故障的时变编队飞行引起的强非线性函数问题，提出了结合模糊神经网络的智能自适应学习机制。此外，还引入了 Nussbaum 函数来处理输入饱和和失效故障。所提出的控制方案的显着特征是同时考虑时变编队飞行、执行器故障（包括偏差和失效故障）、有限的通信范围和输入饱和。通过李亚普诺夫稳定性分析证明，即使在执行器故障的情况下，所有无人机也能实现智慧城市观测的安全编队飞行。使用开源 Pixhawk 自动驾驶仪进行硬件在环实验，以证明所提出的控制方案的有效性。