ENGLISH
La vitrine de diffusion des publications et contributions des chercheurs de l'ÉTS
RECHERCHER

Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

Brahmi, Brahim, Rahman, Mohammad Habibur et Saad, Maarouf. 2023. « Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction ». IET Cyber-systems and Robotics, vol. 5, nº 1.
Compte des citations dans Scopus : 4.

[thumbnail of Saad-M-2023-26466.pdf]
Prévisualisation
PDF
Saad-M-2023-26466.pdf - Version publiée
Licence d'utilisation : Creative Commons CC BY-NC-ND.

Télécharger (1MB) | Prévisualisation

Résumé

This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities. The first step is to estimate the human's target position (also known as TPH). The second step is to develop a robust adaptive impedance control mechanism. A novel Non‐singular Terminal Sliding Mode Control combined with an adaptive super‐twisting controller is being developed to achieve this goal. This combination's purpose is to provide high reliability, continuous performance tracking of the system's trajectories. The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering, which is the sliding mode's most glaring issue. The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network, which allows a robotic exoskeleton to simply track the desired impedance model. To validate the approach in real‐time, an exoskeleton robot was deployed in controlled experimental circumstances. A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.

Type de document: Article publié dans une revue, révisé par les pairs
Professeur:
Professeur
Saad, Maarouf
Affiliation: Génie électrique
Date de dépôt: 30 mai 2023 20:32
Dernière modification: 31 mai 2023 14:31
URI: https://espace2.etsmtl.ca/id/eprint/26466

Actions (Authentification requise)

Dernière vérification avant le dépôt Dernière vérification avant le dépôt