Event‐Triggered Distributed Model Predictive Control of Linear Systems With Additive Disturbances

ABSTRACT This article 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.