Data-driven scaled consensus of linear multi-agent systems using a dynamic event-triggered scheme

Scaled consensus has attracted significant attention for addressing diverse physical network constraints. However, most existing methods rely on accurate system models, which are computationally expensive and impractical in complex environments. This paper investigates the fully data-driven scaled consensus problem for linear multi-agent systems, eliminating the need for explicit system identification. A data-based system representation is constructed directly from noisy state-input measurements. An improved dynamic event-triggered scheme is developed by jointly considering both sampling and transmission states, which further reduces communication frequency. Stability conditions are first derived under model availability and then extended to a purely data-driven setting via the S-procedure. Numerical simulations validate the effectiveness of the proposed method.