ABSTRACT
This chapter presents a refined fault-tolerant synchronization tracking control scheme using fractional-order calculus and intelligent learning architecture for fixed-wing unmanned aerial vehicles against actuator and sensor faults. To increase the flight safety of unmanned aerial vehicles, a recurrent wavelet fuzzy neural network 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, fractional-order sliding-mode control, involving the adjustable fractional-order operators and the robustness of sliding-mode control, is dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of recurrent wavelet fuzzy neural network learning systems embedded in the fixed-wing unmanned aerial vehicles are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing unmanned aerial vehicles 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.
