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Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification

This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the cont...

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Detalles Bibliográficos
Autores principales: Kaplanoglu, Erkan, Akgun, Gazi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573718/
https://www.ncbi.nlm.nih.gov/pubmed/36236742
http://dx.doi.org/10.3390/s22197645
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author Kaplanoglu, Erkan
Akgun, Gazi
author_facet Kaplanoglu, Erkan
Akgun, Gazi
author_sort Kaplanoglu, Erkan
collection PubMed
description This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems.
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spelling pubmed-95737182022-10-17 Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification Kaplanoglu, Erkan Akgun, Gazi Sensors (Basel) Article This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems. MDPI 2022-10-09 /pmc/articles/PMC9573718/ /pubmed/36236742 http://dx.doi.org/10.3390/s22197645 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kaplanoglu, Erkan
Akgun, Gazi
Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title_full Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title_fullStr Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title_full_unstemmed Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title_short Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification
title_sort data-driven predictive control of exoskeleton for hand rehabilitation with subspace identification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573718/
https://www.ncbi.nlm.nih.gov/pubmed/36236742
http://dx.doi.org/10.3390/s22197645
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