AI-Informed Decentralized Robust Adaptive Control for Low-Altitude UAV Swarms With Dynamic Event-Triggered and Nonlinear Uncertainties

Low-altitude uncrewed aerial vehicle (UAV) swarms require reliable coordination under uncertain dynamics, limited wireless communication resources, and rapidly changing operating conditions. Continuous information exchange can introduce considerable communication overhead, especially when multiple UAVs share bandwidth-constrained networks. This article develops an AI-enabled decentralized networked control framework with dynamic event-triggered communication for low-altitude UAV swarms. A robust adaptive controller is first constructed to accommodate nonlinear coupling effects and unknown time-varying parameters. A lightweight neural predictor is then incorporated into the event-triggering mechanism to adjust communication thresholds according to the predicted evolution of the system error. Consequently, control information is transmitted only when necessary, reducing redundant network updates while maintaining closed-loop control performance. The proposed framework also provides a decentralized implementation in which each UAV relies primarily on locally available information, improving its suitability for scalable swarm systems. Compared with decentralized intermittent-feedback designs for nontriangular, time-varying systems, the framework also uses predicted state error in dynamic communication scheduling. Numerical experiments with interconnected UAV subsystems demonstrate that the proposed strategy maintains bounded system states and control inputs while significantly reducing communication transmissions. In particular, communication reductions of 45.5% and 95.1% are obtained for the two control channels. The results indicate that AI-assisted event-triggered control (ETC) can effectively coordinate control performance and communication efficiency in resource-constrained UAV swarm networks.