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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9573718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kaplanogluerkan datadrivenpredictivecontrolofexoskeletonforhandrehabilitationwithsubspaceidentification AT akgungazi datadrivenpredictivecontrolofexoskeletonforhandrehabilitationwithsubspaceidentification |