Event-Triggered Multigradient Recursive Data-Driven Iterative Learning Tracking Control for Multiagent Systems

This article introduces an event-triggered multigradient recursive data-driven iterative learning control (ETMGR-DDILC) scheme designed for consensus tracking in multiagent systems (MASs). The data-driven control eliminates the need for precise system modeling, relying solely on the input-output data of the system. Meanwhile, compared to traditional single-gradient descent methods, a multigradient descent approach is adopted for the performance function of the consensus observation error, enabling faster error convergence. In addition, the proposed event-triggered mechanism leverages historical error compensation, decay factors, and flexible multi-parameter adjustments to significantly reduce trigger frequency, enhance resource efficiency, and improve robustness in dynamic environments. Finally, simulation results validate the effectiveness of the proposed control scheme, demonstrating improvements in convergence speed and reduced controller updates.