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

• Published in: International journal of robust and nonlinear control
• Main Authors: Du, Shengli, Fang, Fang, Cao, Boqi, Wang, Xue‐Fang
• Format: Journal Article
• Language: English
• Published: 08.10.2025
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.70228

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.