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Erschienen in:
2013 | OriginalPaper | Buchkapitel
EMG-Based Neural Network Control of an Upper-Limb Power-Assist Exoskeleton Robot
verfasst von : Hang Su, Zhijun Li, Guanglin Li, Chenguang Yang
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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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.