Learning-Based Multifunctional Elbow Exoskeleton Control

In this article, 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 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.

Automated summary

This article proposes a learning-based model for multifunctional elbow exoskeleton control, which uses online iterative learning and impedance adaptation mechanisms to achieve predictive and variable compliant joint control. The model is implemented on a lightweight and portable elbow exoskeleton, providing a novel technique for minimal mechatronics and sensing.