Robust Distributed Model-Predictive Control Based on Self-Triggered Mechanism for Nonlinear Asynchronous Multiagent Systems

This paper proposes a self-triggered distributed model predictive control (ST-DMPC) scheme with adaptive prediction horizon for nonlinear multiagent systems (MASs) subject to additive disturbances and asynchronous communication. To address disturbances, we construct a time-varying contraction constraint that explicitly compensates for disturbance-induced deviations; the corresponding deviation set is estimated with reduced conservatism via the Gronwall Lemma. To balance performance and efficiency, a self-triggered mechanism is devised to schedule aperiodic optimizations, coupled with an adaptive horizon strategy that dynamically reduces computational complexity as the system converges. Furthermore, a lengthened sequence transmission strategy is introduced to actively compensate for information lags caused by asynchronous communication. Rigorous theoretical analysis guarantees the recursive feasibility and asymptotic stability of the closed-loop system. Simulation results show the effectiveness of the proposed method.