Abstract
The paper presents the electromyogram (EMG)-based neural network control of an upper-limb power-assist exoskeleton robot, which is proposed to control the robot in accordance with the user’s motion intention. The upper limb rehabilitation exoskeleton is with high precision for co-manipulation tasks of human and robot because of its backdrivability, precise positioning capabilities, and zero backlash due to its harmonic drive transmission (HDT). The novelty of this work is the development of an adaptive neural network modeling and control approach to handle the unknown parameters of the harmonic drive transmission in the robot to facilitate motion control. We have conducted the experiments on human subject to identify the various parameters of the harmonic drive system combining sEMG information signals.
This work is supported in part by the Natural Science Foundation of China under Grants 61174045 and 61111130208, the International Science and Technology Cooperation Program of China under Grant 2011DFA10950, and the Fundamental Research Funds for the Central Universities under Grant 2011ZZ0104, and the Program for New Century Excellent Talents in University No. NCET-12-0195.
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Su, H., Li, Z., Li, G., Yang, C. (2013). EMG-Based Neural Network Control of an Upper-Limb Power-Assist Exoskeleton Robot. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_25
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DOI: https://doi.org/10.1007/978-3-642-39068-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
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