Learning-based Multifunctional Elbow Exoskeleton Control

Xiaofeng Xiong*, Cao Danh Do, Poramate Manoonpong

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

We propose a learning-based model for multifunctional elbow exoskeleton control, i.e., assist- and resist-as-needed (AAN and RAN). The model consists of online iterative learning and impedance adaptation mechanisms for predictive and variable compliant joint control. The model was implemented on a lightweight (0.425 kg) and portable elbow exoskeleton (i.e., POW-EXO) worn by three subjects, respectively. The implementation relies only on internal pose (e.g., joint position) feedback, rather than physical compliant mechanisms (e.g., springs) and external sensors (e.g., electromyography (EMG) or force) typically required by conventional exoskeletons and controllers. The proposed model provides a novel technique to achieve multifunctional exoskeleton control with minimal mechatronics and sensing. Interestingly, its RAN control and POW-EXO as a quantification means, may reveal interactive (mechanical) impedance variance and invariance in human motor control.
Original languageEnglish
JournalI E E E Transactions on Industrial Electronics
Number of pages9
ISSN0278-0046
DOIs
Publication statusE-pub ahead of print - 2021

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