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Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot

Javad Omrani1, Majid M Moghaddam1

  • 1Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

A novel adaptive impedance controller using Time Delay Estimation (TDE) was developed for upper limb rehabilitation robots. This controller ensures stable trajectory tracking and improves rehabilitation performance by adapting to unknown dynamics.

Keywords:
Impedance controlLyapunov stabilityadaptive controlrehabilitation robot

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Area of Science:

  • Robotics
  • Control Systems
  • Biomedical Engineering

Background:

  • Upper limb rehabilitation requires precise robotic control to ensure patient safety and effective therapy.
  • Existing controllers often struggle with unknown system dynamics and uncertainties inherent in human-robot interaction.
  • Model reference adaptive control offers a promising approach to address these challenges.

Purpose of the Study:

  • To develop and evaluate a nonlinear Time Delay Estimation (TDE) based model reference adaptive impedance controller for the Tarbiat Modares University Upper Limb Rehabilitation Robot (TUERR).
  • To ensure stable trajectory tracking and adapt to unknown system dynamics and uncertainties.
  • To enhance the rehabilitation process through a performance-based velocity profile.

Main Methods:

  • Development of a TDE-based model reference adaptive impedance controller utilizing a stable reference impedance model.
  • Estimation of unknown system dynamics and uncertainties using TDE.
  • Implementation of an adaptation law to modify controller gains.
  • Analysis of trajectory tracking error boundedness using a Lyapunov function.
  • Introduction of a performance-based velocity profile for trajectory planning.

Main Results:

  • The proposed controller demonstrated stable trajectory tracking for the robot's end-effector.
  • The controller successfully estimated unknown system dynamics and uncertainties.
  • Lyapunov analysis confirmed bounded trajectory tracking errors.
  • The performance-based velocity profile effectively modified trajectory planning based on path deviation.
  • Experimental validation confirmed the controller's effectiveness in the rehabilitation process.

Conclusions:

  • The TDE-based model reference adaptive impedance controller is effective for upper limb rehabilitation robots.
  • The controller provides stable and adaptive control, improving trajectory tracking accuracy.
  • The integration of a performance-based velocity profile enhances the rehabilitation process.
  • Experimental results validate the proposed controller's suitability for practical application in rehabilitation robotics.