This paper presents an event-triggered distributed model predictive control (DMPC) framework for discrete-time linear systems subject to additive bounded disturbances and dynamic couplings. Each subsystem uses a nominal model to formulate a local optimal control problem and employs an error-based triggering condition that accounts for both its own state prediction error and asynchronously received neighbor predictions. To mitigate additive disturbances, we employ a dual-mode strategy that applies the MPC law outside the terminal set and switches to a fixed linear feedback law within it to maintain invariance. Explicit conditions that ensure recursive feasibility, closed-loop stability, and convergence to a disturbance-invariant set are rigorously derived. Two illustrative case studies demonstrate that the proposed method markedly reduces triggering frequency while preserving control performance under asynchronous information exchange.

This paper presents an event-triggered model predictive control scheme for distributed linear systems with additional disturbances. The subsystem states are coupled with each other and affected by unknown bounded disturbances. The communication among subsystems is assumed to be prompt and free from any information loss. A novel distributed event-triggered strategy is developed to balance communication resources and system control performance during asynchronous communication. This mechanism is meticulously designed to ensure optimal system performance while utilizing communication resources. The nominal system is introduced to construct a local optimization problem and a triggering mechanism considering the coupling influence is developed. To counter the additional disturbances, the dual-mode control approach has been implemented along with developing a robust terminal set. The terminal set is purposefully designed to maintain system stability in the presence of additive disturbances, achieved through a meticulously designed triggering mechanism. Then it is imperative to discuss the stability of the resulting closed-loop system and provide a proof process of the feasibility of the iterative optimization. Finally, the effectiveness of the proposed algorithm is validated through simulation results, thereby confirming its efficacy.