Direct Data-Driven Consensus Control of Self-Triggered Multi-Agent Systems: A Switched Systems Approach

This paper addresses the self-triggered consensus control (STC) problem for unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) have gained popularity in the context of MASs due to their ability to eliminate the need for continuous monitoring and reduce communication demands. However, conventional STM designs typically rely on explicit model knowledge, which is often difficult to obtain in practical applications. To overcome this limitation, we propose a data-driven framework for synthesizing an STC protocol that integrates an STM with a state feedback control law. First, by interpreting a self-triggered MAS as a switched MAS, we develop a system lifting technique to construct a data-based representation for the MAS. Leveraging Petersen's lemma, a stabilizing controller and the self-triggering matrix are codesigned by formulating a data-based linear matrix inequality (LMI), while ensuring the stability of the closed-loop system. Simulation results validate the effectiveness of the proposed data-driven approach.