Model Predictive Control for Human-Centred Lower Limb Robotic Assistance

TL;DR

This paper presents a novel MPC architecture with fuzzy logic for adaptive, human-centered control of lower limb exoskeletons, enabling real-time assistance mode transitions based on EMG signals for improved rehabilitation outcomes.

Contribution

It introduces a fuzzy logic-based assistance mode selection integrated with model predictive control, tailored for human-robot synergy in lower limb exoskeletons, addressing variability among patients.

Findings

Successful hardware demonstration with three subjects.

Effective real-time mode transitions based on EMG signals.

Satisfactory assistive performance in all modes.

Abstract

Loss of mobility or balance resulting from neural trauma is a critical consideration in public health. Robotic exoskeletons hold great potential for rehabilitation and assisted movement, yet optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons centred around a fuzzy logic algorithm (FLA) identifying modes of assistance based on human involvement. Assistance modes are: 1) passive for human relaxed and robot dominant, 2) active-assist for human cooperation with the task, and 3) safety in the case of human resistance to the robot. Human torque is estimated from electromyography (EMG) signals prior to joint motions, enabling advanced prediction of torque by the MPC and selection of assistance mode by the FLA. The controller is demonstrated in hardware with three subjects on a 1-DOF knee exoskeleton tracking a sinusoidal trajectory with human relaxed assistive, and resistive. Experimental results show quick and appropriate transfers among the assistance modes and satisfied assistive performance in each mode. Results illustrate an objective approach to lower limb robotic assistance through on-the-fly transition between modes of movement, providing a new level of human-robot synergy for mobility assist and rehabilitation.