A fixed-time fault-tolerant tracking control for fractional-order UAV networks using adaptive fuzzy neural and event-triggered mechanisms
Scientific Reports, Published online: 05 August 2026; doi:10.1038/s41598-026-65044-z A fixed-time fault-tolerant tracking control for fractional-order UAV networks using adaptive fuzzy neural and event-triggered mechanisms
This research explores the coordination of tracking control for fractional-order fixed-wing unmanned aerial vehicle (UAV) networks, while accounting for various issues such as actuator faults, input saturation, unknown nonlinear dynamics, external disturbances, and communication limitations. The focus is on developing a dynamic memory event-triggered fixed-time fault-tolerant control framework to enhance tracking accuracy, accommodate faults, and optimize communication efficiency.
Initially, the authors construct a fractional-order coordinated tracking model for the networked fixed-wing UAVs. Subsequently, they employ an adaptive fuzzy neural network to approximate the unknown nonlinear terms without necessitating precise model information. To minimize superfluous information transmission, the researchers introduce a dynamic memory event-triggered mechanism that leverages both the current triggering error and stored memory information.
Furthermore, a fault-tolerant compensation strategy is devised to address actuator faults and saturation-induced nonlinearities. Drawing upon fractional-order Lyapunov analysis and practical fixed-time stability theory, the authors derive sufficient conditions to ensure that the tracking errors converge to a bounded region within a settling time that is independent of initial conditions.
The effectiveness, robustness, and communication-saving prowess of the proposed control methodology are confirmed through simulation results pertaining to networked fixed-wing UAVs. The researchers express gratitude to Qassim University for their financial support (QU-APC-2026). The contributing institutions from Saudi Arabia, Yemen, and Pakistan join in acknowledging no conflicts of interest, and Springer Nature maintains neutrality concerning jurisdictional claims in published maps and institutional affiliations. This article is licensed under the Creative Commons Attribution 4.0 International License.
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