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Consensus Performance of First-Order Agents

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This article concerns consensus problems of linear time-invariant systems with stochastic noises and deterministic disturbances. We consider first-order dynamic agents interconnected by a directed graph network subject to interagent time delay, and we seek to determine the error performance achievable by the consensus feedback protocol, whereas the performance quantifies the disruption of consensus by noises and disturbances. The H2 and H norms of the multiagent system transfer function matrices are employed as measures of the consensus error. For the H2 consensus performance, we obtain an analytical expression of the consensus error, while for the H consensus performance, we show that it can be determined by solving a sequence of independent unimodal quasi-convex problems. The results help demonstrate how, in the presence of stochastic noises and deterministic disturbances, the agent’s unstable dynamics may interact over a delayed network to limit the consensus performance attainable. © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Original languageEnglish
Pages (from-to)5446-5453
JournalIEEE Transactions on Automatic Control
Volume69
Issue number8
Online published6 Feb 2024
DOIs
Publication statusPublished - Aug 2024

Funding

This research was supported in part by the National Natural Science Foundation of China under Grants 62121004 and 62273152, in part by the Guangdong Natural Science Foundation under Grant 2023A1515012747, in part by the Hong Kong RGC under Project CityU 11203321, CityU 11213322, and in part by City University of Hong Kong under Project 9380054.

Research Keywords

  • H2 and H∞ performance
  • consensus
  • Delay effects
  • delay networks
  • Delays
  • Eigenvalues and eigenfunctions
  • Measurement uncertainty
  • Noise measurement
  • Robustness
  • Robustness analysis
  • Transfer functions
  • multiagent systems (MASs)

RGC Funding Information

  • RGC-funded

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