Robust model predictive control for perturbed nonlinear multi-agent systems via dynamic event-triggered scheme

A model predictive control approachintegrated with a dynamic event-triggered strategy is investigated to address the formation stabilization problem of nonlinear multi-agent systems subject to input constraints and additive disturbances. Specifically, a dynamic-type event-triggered mechanism is proposed, in which the triggering threshold is dynamically adjusted within a predefined interval by a dynamic variable. Under this mechanism, the optimization problem is solved only at triggering instants, significantly reducing computational resources while ensuring satisfactory system performance. In addition, a prediction horizon shrinkage strategy is introduced within the model predictive control framework, along with the formulation of a robustness constraint. Consequently, the designed optimization problem achieves reduced computational complexity while effectively handling external disturbances. Through rigorous analysis, theoretical results are established to ensure the recursive feasibility of the overall algorithm and the stability of the resulting closed-loop system. Finally, a numerical example and comparative studies with existing approaches are presented to demonstrate the effectiveness and superiority of the proposed method.