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

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 uncertaint...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-10, Vol.24 (20), p.6546
Main Authors: Espinosa-Espejel, Karina I, Rosales-Luengas, Yukio, Salazar, Sergio, Lopéz-Gutiérrez, Ricardo, Lozano, Rogelio
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Neural Networks
Computer Simulation
Estimates
Exoskeleton Device
external disturbances
Fitness equipment
Humans
Lower Extremity - physiology
Lower Extremity - physiopathology
lower-limb exoskeleton
Neural networks
Neural Networks, Computer
Numerical analysis
Patients
Rehabilitation
Robotics
Robots
Simulation methods
Walking - physiology
walking rehabilitation
Weightlifting
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24206546