<p>This paper investigates the consensus problem of nonlinear multi-agent systems with input saturation, using a novel sampled-data-based event-triggered mechanism. A composite Laplacian quadratic function framework is introduced to more accurately estimate the boundaries of the domain of attraction, offering an enhanced range estimation compared to the conventional Lyapunov quadratic function. The use of incremental quadratic constraints allows for a more precise characterization of the system’s nonlinear dynamics, thereby extending the range of nonlinear terms. Sufficient conditions for a state feedback controller, derived using linear matrix inequalities, are provided. An optimization problem for improving the range estimation of the domain of attraction is then formulated. Finally, the effectiveness of the proposed approach is demonstrated through a simulation example.</p>