A fixed-time fault-tolerant tracking control for fractional-order UAV networks using adaptive fuzzy neural and event-triggered mechanisms

Abstract

Abstract This paper investigates coordinated tracking control for fractional-order fixed-wing unmanned aerial vehicle (UAV) networks subject to actuator faults, input saturation, unknown nonlinear dynamics, external disturbances, and communication constraints. A dynamic memory event-triggered fixed-time fault-tolerant control framework is developed to improve tracking accuracy, fault accommodation, and communication efficiency. First, a fractional-order coordinated tracking model is formulated for networked fixed-wing UAVs. Then, an adaptive fuzzy neural network is used to approximate unknown nonlinear terms without requiring exact model information. To reduce unnecessary information exchange, a dynamic memory event-triggered mechanism is introduced by incorporating both the current triggering error and stored memory information. Moreover, a fault-tolerant compensation strategy is designed to handle actuator faults and saturation-induced nonlinearities. Based on fractional-order Lyapunov analysis and practical fixed-time stability theory, sufficient conditions are derived to guarantee that the tracking errors converge to a bounded neighborhood within a settling time independent of the initial conditions. Simulation results for networked fixed-wing UAVs verify the effectiveness, robustness, and communication-saving performance of the proposed control method.