A Simulation Study of Functional Electrical Stimulation for An Upper Limb Rehabilitation Robot using Iterative Learning Control (ILC) and Linear models

TL;DR

This study develops and tests a linear model-based iterative learning control system with constraints for upper limb stroke rehabilitation, demonstrating improved accuracy and safety in simulated tasks.

Contribution

It introduces a novel P-ILC approach with derivative constraints for stroke rehab robots, ensuring velocity limits and improving control accuracy.

Findings

Displacement error of 0.0060 m achieved after 16 iterations

System maintains velocity limits during control

Control configurations improve trajectory accuracy

Abstract

A proportional iterative learning control (P-ILC) for linear models of an existing hybrid stroke rehabilitation scheme is implemented for elbow extension/flexion during a rehabilitative task. Owing to transient error growth problem of P-ILC, a learning derivative constraint controller was included to ensure that the controlled system does not exceed a predefined velocity limit at every trial. To achieve this, linear transfer function models of the robot end-effector interaction with a stroke subject (plant) and muscle response to stimulation controllers were developed. A straight-line point-point trajectory of 0 - 0.3 m range served as the reference task space trajectory for the plant, feedforward, and feedback stimulation controllers. At each trial, a SAT-based bounded error derivative ILC algorithm served as the learning constraint controller. Three control configurations were developed and simulated. The system performance was evaluated using the root means square error (RMSE) and normalized RMSE. At different ILC gains over 16 iterations, a displacement error of 0.0060 m was obtained when control configurations were combined.