Distributed Scalable Control for Large-Scale Networked Systems Using Chordal Decomposition

This letter proposes a distributed scalable control scheme for large-scale networked systems using chordal decomposition. To overcome the limitations caused by coupled subsystem structures, we develop a modeling framework for large-scale networked systems with arbitrary topological connections over directed graphs. Subsequently, leveraging the chordal decomposition theorem, we derive a sufficient condition for distributed scalable stabilization of such systems. Unlike traditional centralized or distributed control methods, the proposed approach inherently supports plug-and-play operations. Specifically, it eliminates the need to redesign all controller gains when the system undergoes partial modifications. Furthermore, the method improves computational efficiency while maintaining data confidentiality. Finally, the effectiveness of the proposed approach is validated through a coupled microgrid test system.

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Publication Details

Journal
IEEE Control Systems Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lcsys.2025.3586918
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Distributed Scalable Control for Large-Scale Networked Systems Using Chordal Decomposition

An Lin, Jun Cheng, Chen Peng
IEEE Control Systems Letters
Neural Networks Stability and Synchronization
article

Distributed Scalable Control for Large-Scale Networked Systems Using Chordal Decomposition

An Lin, Jun Cheng, Chen Peng
article en

Abstract

This letter proposes a distributed scalable control scheme for large-scale networked systems using chordal decomposition. To overcome the limitations caused by coupled subsystem structures, we develop a modeling framework for large-scale networked systems with arbitrary topological connections over directed graphs. Subsequently, leveraging the chordal decomposition theorem, we derive a sufficient condition for distributed scalable stabilization of such systems. Unlike traditional centralized or distributed control methods, the proposed approach inherently supports plug-and-play operations. Specifically, it eliminates the need to redesign all controller gains when the system undergoes partial modifications. Furthermore, the method improves computational efficiency while maintaining data confidentiality. Finally, the effectiveness of the proposed approach is validated through a coupled microgrid test system.

IEEE Control Systems LettersVol. 9
Shanghai University (CN), Guangxi Normal University (CN)
National Natural Science Foundation of China, Foundation for Innovative Research Groups of the National Natural Science Foundation of China
Openalex Percentile: Top 82%
Neural Networks Stability and Synchronization
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