A robust adaptive neural controller to drive a knee joint actuated orthosis

This paper presents a robust adaptive control of an actuated orthosis intended to assist the lower limb movements of dependent persons. The proposed controller, based on a MultiLayer Perception Neural Network (MLPNN) and considered as a black-box, does not require the dynamic model of lower limbs/or...

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Bibliographic Details
Published in2012 IEEE International Conference on Robotics and Biomimetics pp. 1656 - 1661
Main Authors Mefoued, S., Daachi, M. E., Daachi, B., Mohammed, S., Amirat, Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Actuated orthosis
Adaptive control
Lyapunov theory/Stability analysis
MultiLayer Perception Neural Network
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ISBN1467321257
9781467321259
DOI10.1109/ROBIO.2012.6491205

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Summary:This paper presents a robust adaptive control of an actuated orthosis intended to assist the lower limb movements of dependent persons. The proposed controller, based on a MultiLayer Perception Neural Network (MLPNN) and considered as a black-box, does not require the dynamic model of lower limbs/orthosis. A neural identification is used to extract the principal components of the MLPNN input vector. The MLPNN is used to compensate the dynamic effects arising from the interaction between the human lower limb and the orthosis. MLPNN weights are adjusted online according to an adaption algorithm based on the Lyapunov analysis. Experiments, carried out on a healthy subject, show the good performance of the proposed controller in terms of trajectory tracking and robustness against external disturbances.
ISBN:1467321257
9781467321259
DOI:10.1109/ROBIO.2012.6491205