Model Reduction Methods for Complex Network Systems

X. Cheng*, J. M. A. Scherpen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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Abstract

Network systems consist of subsystems and their interconnections and provide a powerful framework for the analysis, modeling, and control of complex systems. However, subsystems may have high-dimensional dynamics and a large number of complex interconnections, and it is therefore relevant to study reduction methods for network systems. Here, we provide an overview of reduction methods for both the topological (interconnection) structure of a network and the dynamics of the nodes while preserving structural properties of the network. We first review topological complexity reduction methods based on graph clustering and aggregation, producing a reduced-order network model. Next, we consider reduction of the nodal dynamics using extensions of classical methods while preserving the stability and synchronization properties. Finally, we present a structure-preserving generalized balancing method for simultaneously simplifying the topological structure and the order of the nodal dynamics.

Original languageEnglish
Title of host publicationAnnual review of control, robots, and Aauthonomous systems, VOL 4, 2021
EditorsNE Leonard
PublisherAnnual Reviews
Pages425-453
Number of pages29
DOIs
Publication statusPublished - May-2021

Publication series

NameAnnual Review of Control Robotics and Autonomous Systems
PublisherANNUAL REVIEWS
Volume4
ISSN (Print)2573-5144

Keywords

  • reduced-order modeling
  • network systems
  • interconnected systems
  • multiagent systems
  • graph clustering
  • synchronization
  • semistability
  • structure-preserving
  • ORDER REDUCTION
  • BALANCED TRUNCATION
  • MULTIAGENT SYSTEMS
  • POWER NETWORKS
  • CONSENSUS
  • SYNCHRONIZATION
  • AGGREGATION
  • REALIZATION
  • PASSIVITY
  • TUTORIAL

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