A data-driven approach to distributed modal consensus and synchronization
Andrea Monti, Sergio Galeani, Corrado Possieri, Mario Sassano · 2022 IEEE 61st Conference on Decision and Control (CDC) · 2022
In this paper, we propose a data-driven control strategy to solve the distributed modal consensus and synchronization problems. The proposed solution relies only on input/output data and does not require any knowledge of the dynamics of each agent. Furthermore, it is shown that the synchronization task requires to address the problem of transferring, in finite time, the state of a system from an initial state to a terminal one in a completely data-driven framework, which is therefore tackled and solved here. The above concepts are then illustrated via a multi-agent system consisting of RC circuits.