Consensus-Based Distributed Optimization Enhanced by Integral Feedback

Xuan Wang*, Shaoshuai Mou, Brian D.O. Anderson

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)

    Abstract

    Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multiagent networks to solve convex optimization problems with local linear constraints. Assuming agent interactions are modeled by an undirected graph, the algorithm is capable of achieving the optimum solution with an exponential convergence rate. Furthermore, inherited from the beneficial integral feedback, the proposed algorithm has attractive requirements on communication bandwidth and good robustness against disturbance. Both analytical proof and numerical simulations are provided to validate the effectiveness of the proposed distributed algorithms in solving constrained optimization problems.

    Original languageEnglish
    Pages (from-to)1894-1901
    Number of pages8
    JournalIEEE Transactions on Automatic Control
    Volume68
    Issue number3
    DOIs
    Publication statusPublished - 1 Mar 2023

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