Model-independent rendezvous of Euler-Lagrange agents on directed networks

Mengbin Ye, Changbin Yu, Brian D.O. Anderson

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    8 Citations (Scopus)

    Abstract

    This paper proposes a distributed, modelindependent algorithm to achieve rendezvous to a stationary leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics. We show that if the directed graph contains a directed spanning tree, with the leader as the root node and with no incoming edges, then a model-independent algorithm semi-globally achieves the rendezvous objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the parameters of the self-dynamics of any agent in the network. For stability, a pair of control gain terms for each agent are required to meet several inequalities and so design of the algorithm requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.

    Original languageEnglish
    Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3499-3505
    Number of pages7
    ISBN (Electronic)9781479978861
    DOIs
    Publication statusPublished - 8 Feb 2015
    Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
    Duration: 15 Dec 201518 Dec 2015

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    Volume54rd IEEE Conference on Decision and Control,CDC 2015
    ISSN (Print)0743-1546
    ISSN (Electronic)2576-2370

    Conference

    Conference54th IEEE Conference on Decision and Control, CDC 2015
    Country/TerritoryJapan
    CityOsaka
    Period15/12/1518/12/15

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