Robust sampled-data fuzzy consensus control for nonlinear multi-agent systems with parametric uncertainties

Abstract

This study proposes a sampled-data fuzzy consensus control technique for nonlinear multi-agent systems (MAS) containing parametric uncertainties. First, the nonlinear MAS with parametric uncertainties is modeled as a Takagi–Sugeno (T–S) fuzzy system. And then, the study introduces error dynamics, ensuring leader-following consensus, through the application of graph theory. To obtain relaxed sufficient conditions guaranteeing robust stabilization, this research incorporates improved membership function-dependent (MFD) $H_{\infty }$ criteria and a two-sided looped-functional. The resulting robust controller design conditions are expressed in the form of linear matrix inequalities (LMIs). Finally, numerical simulation examples are given to illustrate the superior performance and feasibility of the proposed design technique.