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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. [ABSTRACT FROM AUTHOR]

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

As targets can often be treated as randomly rough surfaces in optical bands, light scattered from them carries rich information, therefore, light scattering analysis has been used in various applications such as remote sensing and target identification. Compared to experimental light scattering detection, numerical light scattering computation is still an essential tool to understand the functions of randomly rough surface features on scattered light in the theoretical level. However, classical numerical methods rely on statistical process consisting of massive randomly rough surface generation, scattered light calculation and ensemble average computation to obtain stable results of light scattering fields, therefore they suffer from heavy computation load and low processing efficiency. To solve such problem, deep neural networks are designed here to directly obtain the stable light scattering fields but completely avoid the statistical process; and an example of reconstructing light scattering fields from 1-D randomly rough surfaces is reported. Compared to the classical Kirchhoff approximation method in high accuracy but low efficiency, the proposed deep learning method not only provides accurate light scattering results, but also remarkably accelerates the processing speed, thus opens a new way for numerical light scattering computation in broader scopes. [ABSTRACT FROM AUTHOR]