Neural-network-enhanced torque estimation control of a soft wearable exoskeleton for elbow assistance

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.