Journal
MECHATRONICS
Volume 63, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechatronics.2019.102279
Keywords
Assistance efficiency; Neural-network-enhanced; sEMG; Soft wearable exoskeleton; Torque estimation control
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Funding
- National Natural Science Foundation of China [51705240]
- China Postdoctoral Science Foundation [2018M640480]
- Natural Science Foundation of Jiangsu Province of China [BK20170783]
- State Key Laboratory of Robotics and System (HIT) [SKLRS-2018-KF10]
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Soft wearable exoskeletons are a new approach for the applications of power assistance and rehabilitation training. In the present work, a neural-network-enhanced torque estimation controller (NNETEC) is proposed for a soft wearable elbow assistance exoskeleton with compliant tendon-sheath actuator. A comprehensive overview for the major components of the soft exoskeleton is introduced. The locations of anchor points are optimized via the maximum-stiffness principle. The NNETEC strategy is developed by fusing the feedback signals from surface electromyography (sEMG) sensors, inertial measurement units, force sensors, and motor encoder. It consists of a joint torque estimation module to identify the elbow torque of wearer based on Kalman filter, a neural-network adjustment module to recognize human motion intention, and a proportional-integral-derivative controller with hybrid position/torque feedbacks. Further experimental investigations are carried out by five volunteers to validate the effectiveness of the proposed soft elbow exoskeleton and control strategy. The results of the dumbbell-lifting experiments with various weights and frequencies demonstrate that, when compared with the proportional control strategy and the sEMG-based assistive control strategy without neural-network adjustment, the developed NNETEC method can achieve higher power assistance efficiency.
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