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    Leader Tracking of Euler-Lagrange Agents on Directed Switching Networks Using a Model-Independent Algorithm

    Access Status
    Open access
    Authors
    Ye, Mengbin
    Anderson, B.D.O.
    Yu, C.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Ye, M. and Anderson, B.D.O. and Yu, C. 2019. Leader Tracking of Euler-Lagrange Agents on Directed Switching Networks Using a Model-Independent Algorithm. IEEE Transactions on Control of Network Systems. 6 (2): pp. 561-571.
    Source Title
    IEEE Transactions on Control of Network Systems
    DOI
    10.1109/TCNS.2018.2856298
    ISSN
    2325-5870
    Faculty
    Faculty of Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104500
    Remarks

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/84353
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose a discontinuous distributed model-independent algorithm for a directed network of Euler-Lagrange agents to track the trajectory of a leader with nonconstant velocity. We initially study a fixed network and show that the leader tracking objective is achieved semiglobally exponentially fast if the graph contains a directed spanning tree. By model independent, we mean that each agent executes its algorithm with no knowledge of the parameter values of any agent's dynamics. Certain bounds on the agent dynamics (including any disturbances) and network topology information are used to design the control gain. This fact, combined with the algorithm's model independence, results in robustness to disturbances and modeling uncertainties. Next, a continuous approximation of the algorithm is proposed, which achieves practical tracking with an adjustable tracking error. Last, we show that the algorithm is stable for networks that switch with an explicitly computable dwell time. Numerical simulations are given to show the algorithm's effectiveness.

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