Investigation of consensus for nonlinear time-varying multiagent systems via data-driven techniques

Abstract

This paper employs data-driven techniques to investigate the robustness control of leader-follower consensus in nonlinear discrete-time time-varying multiagent systems with fixed topology. Initially, pertinent symbolic definitions for sampled data are established, followed by an introduction to graph theory and system models. As data-driven algorithms necessitate linear systems, each nonlinear subsystem is linearized. Subsequently, distributed controllers are designed based on control principles to ensure multi-agent consensus. Additionally, the controller gain matrix is derived via a data-driven method, with its feasibility theoretically verified by solving nonlinear matrix inequalities. Finally, numerical simulations validate the efficacy of this approach for achieving robust leader-follower consensus control.