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

Le résumé fourni par la source

This paper employs data-driven techniques to investigate the robustness control of leader-followers consensus in nonlinear discrete time-varying multi-agent systems with a fixed topology. Initially, pertinent symbolic definitions for the sampled data are established, followed by an introduction to graph theory and system models. Given that data-driven algorithms necessitate linear systems, it is imperative to linearize each nonlinear system. Subsequently, distributed controllers are developed based on distributed control principles to ensure consensus in multi-agent systems. Additionally, the controller gain matrix is derived through a data-driven method, and its feasibility is theoretically examined by solving a nonlinear matrix inequality. Ultimately, numerical simulations validate the efficacy of this data-driven approach for achieving robust leader-followers consensus control.

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