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Active Disturbance Rejection Control via Neural Networks for a Lower-Limb Exoskeleton.

Karina I Espinosa-Espejel1, Yukio Rosales-Luengas1, Sergio Salazar1

  • 1Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, Mexico.

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|October 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an Artificial Neural Network (ANN) controller for lower-limb exoskeletons in rehabilitation. The novel approach effectively manages uncertainties and disturbances during walking, enhancing patient recovery.

Keywords:
Artificial Neural Networksexternal disturbanceslower-limb exoskeletonwalking rehabilitation

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Lower-limb exoskeletons are crucial for rehabilitation.
  • Patient-exoskeleton interaction introduces model uncertainties and external disturbances.
  • Existing control methods struggle with unknown non-linear dynamics.

Purpose of the Study:

  • To design an Artificial Neural Network (ANN) based control algorithm for lower-limb exoskeletons.
  • To address model uncertainties and external disturbances in exoskeleton control.
  • To enable natural walking trajectories during lower-limb rehabilitation.

Main Methods:

  • Developed a control algorithm utilizing Artificial Neural Networks (ANNs).
  • Employed active disturbance rejection control (ADRC) via ANNs to estimate and compensate for unknown non-linear dynamics and disturbances.
  • Validated the ANN-controller through numerical simulations and experimental implementation.

Main Results:

  • The ANN-based controller effectively managed unknown non-linear dynamics.
  • Active disturbance rejection control successfully estimated and compensated for external disturbances.
  • Both numerical simulations and experimental results demonstrated the viability of the proposed ANN-controller.

Conclusions:

  • The proposed ANN-based control algorithm is a promising approach for lower-limb exoskeleton rehabilitation.
  • The integration of ANNs with active disturbance rejection control enhances robustness against uncertainties and disturbances.
  • This technology has the potential to improve walking recovery in patients undergoing lower-limb rehabilitation.