A Novel Asymmetric Functional Approach on Sampled-Data-Based Exponential Consensus of Nonlinear Multi-Agent Systems Against FDI Attacks

This article investigates the exponential consensus in leaderless multi-agent systems (MASs) subject to Lipschitz nonlinearity, external perturbations, and missing measurements. First, an aperiodic nonfragile sampled-data control strategy is applied to the MASs in the presence of communication delays and randomly occurring false data injection attacks. This protocol provides robustness against controller gain fluctuations and enhances consensus performance with $H_\infty$ attenuation level. Next, unlike the existing studies, a novel exponential-type asymmetric Lyapunov-Krasovskii functional and a two-sided looped functional are constructed together with the relaxation of positive definiteness for an individual matrix. Utilizing these functionals, exponential consensus conditions are obtained within the form of linear matrix inequalities. Finally, using the YALMIP toolbox in MATLAB, three numerical examples validate theoretical outcomes exhibiting reduced conservatism with improved percentage of performance by maximizing the sampling period with a minimum number of decision variables compared with existing literature.