Neural-network-enhanced torque estimation control of a soft wearable exoskeleton for elbow assistance. (November 2019)

• Record Type: Journal Article
• Authors: Wu, Qingcong; Chen, Bai; Wu, Hongtao
• Is Part Of: Mechatronics. Volume 63(2019)
• Publication Date: 2019-11
• Subjects: Assistance efficiency -- Neural-network-enhanced -- sEMG -- Soft wearable exoskeleton -- Torque estimation control; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2019.102279
• Languages: English
• ISSNs: 0957-4158

Abstract

Highlights: A soft wearable exoskeleton with compliance tendon-sheath actuation is developed for elbow assistance. A neural-network-enhanced torque estimation controller is proposed to improve assistance efficiency. The effectiveness is validated by dumbbell-lifting experiments with various weights and frequencies. The results are compared with proportional control strategy and sEMG-based assistive control strategy. Abstract: 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.