Distributed Dynamic Event-Triggered Formation Tracking Control for Multiagent Systems via a Unified Update Mechanism

This article investigates the distributed formation tracking control of nonlinear multiagent systems (MASs) in the presence of unknown dynamics and external disturbances, with a particular focus on communication efficiency. To simultaneously address accurate estimation and communication savings, a unified dynamic event-triggered framework integrating observer and controller updates is proposed. In particular, an event-triggered extended state observer (ETESO) enhanced by a bias radial basis function neural network (BRBFNN) is developed, capable of capturing system uncertainties and external disturbances. Subsequently, a distributed dynamic event-triggered control scheme is designed, enabling each agent to update its ETESO and controller simultaneously and adaptively through dynamic triggering thresholds. Rigorous theoretical analysis is provided to confirm the semiglobal uniform ultimate boundedness (SGUUB) of the closed-loop system. Compared with existing fixed-threshold or controller-only triggering schemes, simulation results confirm that the proposed unified update framework simplifies system architecture while significantly enhancing estimation accuracy, tracking performance, and communication efficiency.