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

Sensors (Basel). 2024 Oct 11;24(20):6546. doi: 10.3390/s24206546.

Abstract

This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed.

Keywords: Artificial Neural Networks; external disturbances; lower-limb exoskeleton; walking rehabilitation.

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Exoskeleton Device*
  • Humans
  • Lower Extremity* / physiology
  • Lower Extremity* / physiopathology
  • Neural Networks, Computer*
  • Walking / physiology

Grants and funding

This research received no external funding.