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    Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

    264664.pdf (1.037Mb)
    Access Status
    Open access
    Authors
    Rahimi, H.N.
    Howard, Ian
    Cui, Lei
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Rahimi, H.N. and Howard, I. and Cui, L. 2018. Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints. Mechanical Systems and Signal Processing. 112: pp. 44-60.
    Source Title
    Mechanical Systems and Signal Processing
    DOI
    10.1016/j.ymssp.2018.03.042
    ISSN
    0888-3270
    School
    School of Civil and Mechanical Engineering (CME)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DE170101062
    URI
    http://hdl.handle.net/20.500.11937/66628
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations.

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