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Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the...

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Autores principales: Hu, Yang, Meng, Jingyan, Li, Guoning, Zhao, Dazheng, Feng, Guang, Zuo, Guokun, Liu, Yunfeng, Zhang, Jiaji, Shi, Changcheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146308/
https://www.ncbi.nlm.nih.gov/pubmed/37112385
http://dx.doi.org/10.3390/s23084042
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author Hu, Yang
Meng, Jingyan
Li, Guoning
Zhao, Dazheng
Feng, Guang
Zuo, Guokun
Liu, Yunfeng
Zhang, Jiaji
Shi, Changcheng
author_facet Hu, Yang
Meng, Jingyan
Li, Guoning
Zhao, Dazheng
Feng, Guang
Zuo, Guokun
Liu, Yunfeng
Zhang, Jiaji
Shi, Changcheng
author_sort Hu, Yang
collection PubMed
description Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability.
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spelling pubmed-101463082023-04-29 Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot Hu, Yang Meng, Jingyan Li, Guoning Zhao, Dazheng Feng, Guang Zuo, Guokun Liu, Yunfeng Zhang, Jiaji Shi, Changcheng Sensors (Basel) Article Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability. MDPI 2023-04-17 /pmc/articles/PMC10146308/ /pubmed/37112385 http://dx.doi.org/10.3390/s23084042 Text en © 2023 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
Hu, Yang
Meng, Jingyan
Li, Guoning
Zhao, Dazheng
Feng, Guang
Zuo, Guokun
Liu, Yunfeng
Zhang, Jiaji
Shi, Changcheng
Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title_full Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title_fullStr Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title_full_unstemmed Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title_short Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot
title_sort fuzzy adaptive passive control strategy design for upper-limb end-effector rehabilitation robot
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146308/
https://www.ncbi.nlm.nih.gov/pubmed/37112385
http://dx.doi.org/10.3390/s23084042
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