Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

TL;DR

This paper introduces a novel feedback control system for a high-DOF exoskeleton that enables paraplegic users to walk hands-free, utilizing advanced optimization and machine learning for robust, stable, and smooth movement.

Contribution

It presents a new control approach that allows stable, hands-free walking in paraplegics using a fully actuated exoskeleton, combining modern optimization and supervised learning techniques.

Findings

Robust velocity regulation achieved in simulation.

Preliminary stability demonstrated in Gazebo environment.

Initial experimental results with paraplegic users show promise.

Abstract

This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.