Adaptive Parameter Control Using AAN for Lower Limb Rehabilitation Exoskeletons

TL;DR

This paper introduces an adaptive assist-as-needed control algorithm for lower limb rehabilitation exoskeletons, improving trajectory tracking and adaptability to patient variability through a novel integration of dynamics modeling, torque estimation, and adaptive control.

Contribution

It presents a new AAN control strategy combining a human-robot coupling model, HTMO, and APC for enhanced exoskeleton assistance, validated through MATLAB/Simulink simulations.

Findings

Improved trajectory tracking accuracy.

Reduced interaction torque oscillations.

Enhanced adaptability to different patient conditions.

Abstract

Exoskeletons play a crucial role in assisting patients with varying mobility levels during rehabilitation. However, existing control strategies face challenges such as imprecise trajectory tracking, interaction torque oscillations, and limited adaptability to diverse patient conditions. To address these issues, this paper proposes an assist-as-needed (AAN) control algorithm that integrates a human-robot coupling dynamics model, a human torque-momentum observer (HTMO), and an adaptive parameter controller (APC). The algorithm first employs inverse dynamics to compute the joint torques required for the rehabilitation trajectory. The HTMO then estimates the torque exerted by the patient's joints and determines the torque error, which the exoskeleton compensates for via a spring-damper system, ultimately generating the target trajectory. Finally, the APC ensures adaptive assistive control. The proposed method is validated for its effectiveness in MATLAB/Simulink.