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    Neural adaptive assist-as-needed control for rehabilitation robots

    Access Status
    Fulltext not available
    Authors
    Rahimi, H.
    Howard, Ian
    Cui, Lei
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Rahimi, H. and Howard, I. and Cui, L. 2016. Neural adaptive assist-as-needed control for rehabilitation robots, pp. 74-80.
    Source Title
    Australasian Conference on Robotics and Automation, ACRA
    ISBN
    9781634396080
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/70043
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 Australasian Robotics and Automation Association. All rights reserved. Robot-assisted therapy can improve motor function in patients recovering from stroke. Assist-as-needed algorithms provide only minimal robotic assistance in the therapy, thus requiring significant effort from the impaired subject. This paper presents an adaptive neural assist-as-needed controller for rehabilitative robots. The controller combines the Lyapunov direct method with the computed torque control and neural networks. Robot assistance is limited to only as needed by adding the force reducing term into the adaptive control law. This paper shows that by the presented method the tracking error converges to a small value around zero while the neural network weights and system uncertainties remain bounded. Simulation on a robot manipulator model is presented to demonstrate the effectiveness of the proposed meth.

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