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Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control.

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  • 1CIAMS, Sport Sciences Department, Université Paris-Saclay, Orsay, France.

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Summary

This study enhances active exoskeleton upper limb weight support by incorporating joint misalignments and a learned control law. This improves estimation accuracy and reduces muscle effort, paving the way for adaptive controllers.

Keywords:
feed-forward controlhuman parameters identificationhuman/exoskeleton interactionjoints misalignmentsrehabilitation roboticsweight support

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Area of Science:

  • Robotics
  • Biomechanics
  • Rehabilitation Engineering

Background:

  • Active exoskeletons offer potential for rehabilitation and worker safety.
  • Upper limb weight support exoskeletons can aid mobility and reduce occupational strain.
  • Existing models often neglect joint misalignments, impacting performance.

Purpose of the Study:

  • To improve upper limb weight support in active exoskeletons.
  • To develop a weight model accounting for joint misalignments.
  • To design a control law with feedforward terms for enhanced performance.

Main Methods:

  • Developed a weight support model incorporating joint misalignments.
  • Implemented a control law with feedforward terms derived from population data.
  • Conducted experiments with 65 participants on posture maintenance and elbow movements.

Main Results:

  • Joint misalignments significantly reduced weight estimation errors and improved reliability.
  • The control architecture minimized model tracking errors across conditions.
  • Weight support reduced antigravity muscle activity but increased elbow extensor activity.

Conclusions:

  • Accounting for joint misalignments enhances exoskeleton weight support accuracy.
  • The proposed control strategy effectively reduces tracking errors.
  • Adaptive weight support controllers show promise for minimizing human effort in exoskeleton applications.