Distributed data driven control for multi-agent consensus with unknown system dynamics

This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study investigates the consensus tracking problem for unknown multi-agent systems with time-varying communication topology using data-driven control and model predictive control methods. The proposed distributed iterative protocol does not require knowledge of the dynamics of the multi-agent systems, utilizing only local input-output data for each agent. Numerical simulations demonstrate the effectiveness of the derived consensus conditions.