RBF-Based Neuro-Adaptive Controller for a Knee Joint Rehabilitation Orthosis

  • Said Talbi
  • Boubaker Daachi
  • Karim Djouani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


In this paper, we address a knee joint orthosis control for rehabilitation purposes. Only the structure of the system’s dynamic model is supposed to be known. Inertia of the knee-shank-orthosis system is identified on-line using an adaptive term. In order to approximate all of the other dynamics (viscous and solid frictions, gravity related torque, etc.), we use an RBF Neural Network (RBFNN) with no off-line prior training. Adaptation laws of the neural parameters and the inertia adaptive term are derived from the closed loop system’s overall stability study using Lyapunov’s theory. The study considers three cases: wearer being completely inactive or applying either a resistive or an assistive torque. Simulation results and conducted analysis show the effectiveness of the proposed approach.


RBFNN neuro-adaptive control rehabilitation knee joint orthosis Lyapunov theory 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Said Talbi
    • 1
  • Boubaker Daachi
    • 1
  • Karim Djouani
    • 1
  1. 1.Laboratoire Images, Signaux et Systèmes Intelligents (LISSI, E. A.3956)University of Paris-Est CréteilCréteilFrance

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