Fully distributed data-driven model-free adaptive control for consensus tracking in multi-agent systems.

This paper introduces a fully distributed model-free adaptive control (MFAC) approach for consensus tracking in multi-agent systems (MASs) with compact form data linearization (CFDL). Unlike prior methods that require agents to know the full communication graph, our approach allows each agent to configure its controller using only local information from its neighbors, achieving a fully distributed control. Therefore, our method easily supports scenarios where agents dynamically join or leave MAS.

A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems.

This paper presents a new data-driven subspace distributed fault detection strategy specifically designed for linear heterogeneous multi-agent systems (MASs). The proposed approach leverages the characteristics of heterogeneous MASs, where agents exhibit diverse dynamics and parameters. By utilizing subspace construction techniques, the proposed method captures the normal behavior of each agent and enables the detection of deviations that indicate the presence of faults.