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