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This paper considers self-triggered consensus control of unknown linearmulti-agent systems (MASs). Self-triggering mechanisms (STMs) are widely usedin MASs, thanks to their advantages in avoiding continuous monitoring andsaving computing and communication resources. However, existing results requirethe knowledge of system matrices, which are difficult to obtain in real-worldsettings. To address this challenge, we present a data-driven approach todesigning STMs for unknown MASs building upon the model-based solutions. Ourapproach leverages a system lifting method, which allows us to derive adata-driven representation for the MAS. Subsequently, a data-drivenself-triggered consensus control (STC) scheme is designed, which combines adata-driven STM with a state feedback control law. We establish a data-basedstability criterion for asymptotic consensus of the closed-loop MAS in terms oflinear matrix inequalities, whose solution provides a matrix for the STM aswell as a stabilizing controller gain. In the presence of externaldisturbances, a model-based STC scheme is put forth for$\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for thedata-driven STC. Numerical tests are conducted to validate the correctness ofthe data- and model-based STC approaches. Our data-driven approach demonstratesa superior trade-off between control performance and communication efficiencyfrom finite, noisy data relative to the system identification-based one.

Self-triggered Consensus Control of Multi-agent Systems from Data