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    Model-independent trajectory tracking of Euler-Lagrange agents on directed networks

    84268.pdf (658.6Kb)
    Access Status
    Open access
    Authors
    Ye, Mengbin
    Anderson, B.D.O.
    Yu, C.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Ye, M. and Anderson, B.D.O. and Yu, C. 2016. Model-independent trajectory tracking of Euler-Lagrange agents on directed networks. In: 55th IEEE Conference on Decision and Control (CDC), 12th Dec 2016, Las Vegas, NV.
    Source Title
    2016 IEEE 55th Conference on Decision and Control, CDC 2016
    Source Conference
    55th IEEE Conference on Decision and Control (CDC)
    DOI
    10.1109/CDC.2016.7799335
    ISBN
    9781509018376
    ISSN
    0743-1546
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104500
    URI
    http://hdl.handle.net/20.500.11937/84364
    Collection
    • Curtin Research Publications
    Abstract

    The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.

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