Work

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

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Robust and Nonlinear Control

DOI: 10.1002/rnc.70228

Authors: Shengli Du, Fang Fang, Boqi Cao, Xue‐Fang Wang

Institutions: Beijing University of Technology, University of Leicester

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,270

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China