Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 25, Issue 6, Pages 577-588Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2016.2582321
Keywords
Transparent control; robotic exoskeleton; energy kernel method of EMG; human-machine interactive mechanism
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Funding
- National Natural Science Foundation of China [61375098]
- National Basic Research Program of China [2011CB013203]
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Transparent control is still highly challenging for robotic exoskeletons, especially when a simple strategy is expected for a large-impedance device. To improve the transparency for late-phase rehabilitation when patient-in-charge mode is necessary, this paper aims at adaptive identification of human motor intent, and proposed an iterative prediction-compensation motion control scheme for an exoskeleton knee joint. Based on the analysis of human-machine interactive mechanism (HMIM) and the semiphenomenological biomechanical model of muscle, an online adaptive predicting controller is designed using a focused time-delay neural network (FTDNN) with the inputs of electromyography (EMG), position and interactive force, where the activation level of muscle is estimated from EMG using a novel energy kernel method. The compensating controller is designed using the normative force-position control paradigm. Initial experiments on the human-machine integrated knee system validated the effectiveness and ease of use of the proposed control scheme.
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