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    Neural network adaptive control design for robot manipulators under velocity constraints

    60031.pdf (946.7Kb)
    Access Status
    Open access
    Authors
    Rahimi Nohooji, Hamed
    Howard, Ian
    Cui, Lei
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Rahimi Nohooji, H. and Howard, I. and Cui, L. 2017. Neural network adaptive control design for robot manipulators under velocity constraints. Journal of the Franklin Institute. 355 (2): pp. 693-713.
    Source Title
    Journal of the Franklin Institute
    DOI
    10.1016/j.jfranklin.2017.11.036
    ISSN
    0016-0032
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/59850
    Collection
    • Curtin Research Publications
    Abstract

    This paper studies the neural adaptive control design for robotic systems with uncertain dynamics under the existence of velocity constraints and input saturation. The control objective is achieved by choosing a control Lyapunov function using joint error variables that are restricted to linear growth and furthermore by introducing a secant type barrier Lyapunov function for constraining the joint rate variables. The former is exploited to bind the forward propagation of the position errors, and the latter is utilized to impose hard bounds on the velocity. Effective input saturation is expressed, and neural networks are employed to tackle the uncertainty problem in the system dynamics. Feasibility conditions are formulated, and the optimal design parameters are obtained by solving the constrained optimization problem. We prove that under the proposed method, semi-global uniform ultimate boundedness of the closed-loop system can be guaranteed. Tracking errors meanwhile converge to small neighborhoods of the origin, and violations of predefined velocity constraints are avoided. Finally, numerical simulations are performed to verify the effectiveness of the theoretical developments.

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