Model‐based event‐triggered leaderless consensus control for nonlinear pure‐feedback multi‐agent systems

Summary This article investigates a model‐based event‐triggered adaptive leaderless consensus control problem for one category of nonlinear pure‐feedback multi‐agent systems (MASs). The implicit function‐based median theorem for decoupling is applied to deal with the over‐fuzzy as well as feedback linearization issues. The feature extraction approach is introduced to solve the difficulty of unequal dimensionality of variables due to the inter‐agents information interaction. Then, by constructing the corresponding adaptive model and utilizing event‐based neural network (NN), a novel distributed design methodology for MAS‐based control input and agent weight‐based dynamic triggering threshold is presented. Through the impulse‐based Lyapunov theory analysis, the designed strategy not just guarantees the stability of the proposed system but then also ensures the boundedness of all signals within the closed‐loop system. Eventually, after verifying the absence of Zeno behavior and ensuring the achievement of the desired consensus tracking, the usefulness of the developed control scheme is justified by a numerical simulation instance.