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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a

Vahidreza Molazadeh1, Qiang Zhang2, Xuefeng Bao3

  • 1Department of Mechanical Engineering and Materials Science at University of Pittsburgh, Pittsburgh, PA, USA.

IEEE Transactions on Control Systems Technology : a Publication of the IEEE Control Systems Society
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid exoskeleton controller combining functional electrical stimulation (FES) and powered assistance. The new adaptive control system significantly improves joint position accuracy for sit-to-stand tasks, aiding mobility disorder recovery.

Keywords:
Functional electrical stimulationHybrid exoskeletonIterative learning controlNeural networksPowered exoskeletonVirtual constraints

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Control Systems

Background:

  • Hybrid exoskeletons integrating functional electrical stimulation (FES) and powered systems offer advanced mobility assistance for individuals with movement disorders.
  • Coordinated FES and exoskeleton use enables active muscle contractions while mitigating FES-induced fatigue through robust torque generation.

Purpose of the Study:

  • To investigate a novel switched distribution of allocation ratios between FES and electric motors within a closed-loop adaptive control framework for hybrid exoskeletons.
  • To develop and validate an iterative learning neural network (NN)-based control law for compensating parametric uncertainties in hybrid exoskeleton models.

Main Methods:

  • A new adaptive controller employing an iterative learning neural network (NN) was designed for a hybrid exoskeleton.
  • A discrete Lyapunov-like stability analysis was performed to prove the asymptotic stability of the switched system with iterative learning.
  • The controller's efficacy was evaluated through sit-to-stand tasks with five human participants, including one with a complete spinal cord injury.

Main Results:

  • The NN-based iterative learning control (NNILC) approach reduced root mean square errors for knee and hip joint positions by 46.20% and 53.34%, respectively, within a few iterations.
  • The controller demonstrated robust trajectory tracking performance despite dynamic switching of allocation levels between FES and electric motors.
  • Comparative analysis showed the NNILC approach outperformed proportional-derivative and traditional iterative learning controllers in accuracy and ease of tuning.

Conclusions:

  • The proposed NNILC strategy effectively manages uncertainties and adapts to changing FES-exoskeleton power distribution, enhancing sit-to-stand task performance.
  • This advanced control method shows potential for simplifying clinical implementation of hybrid exoskeletons by requiring minimal parameter tuning.
  • The study validates the feasibility of hybrid FES-exoskeleton systems with adaptive control for restoring mobility in individuals with severe neurological impairments.