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Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through...

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Detalles Bibliográficos
Autores principales: Zha, Shijia, Li, Tianyi, Cheng, Lidan, Gu, Jihua, Wei, Wei, Lin, Xichuan, Gu, Shaofei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843196/
https://www.ncbi.nlm.nih.gov/pubmed/33552233
http://dx.doi.org/10.1155/2021/8850348
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author Zha, Shijia
Li, Tianyi
Cheng, Lidan
Gu, Jihua
Wei, Wei
Lin, Xichuan
Gu, Shaofei
author_facet Zha, Shijia
Li, Tianyi
Cheng, Lidan
Gu, Jihua
Wei, Wei
Lin, Xichuan
Gu, Shaofei
author_sort Zha, Shijia
collection PubMed
description The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.
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spelling pubmed-78431962021-02-04 Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm Zha, Shijia Li, Tianyi Cheng, Lidan Gu, Jihua Wei, Wei Lin, Xichuan Gu, Shaofei Appl Bionics Biomech Research Article The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton. Hindawi 2021-01-21 /pmc/articles/PMC7843196/ /pubmed/33552233 http://dx.doi.org/10.1155/2021/8850348 Text en Copyright © 2021 Shijia Zha et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zha, Shijia
Li, Tianyi
Cheng, Lidan
Gu, Jihua
Wei, Wei
Lin, Xichuan
Gu, Shaofei
Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title_full Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title_fullStr Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title_full_unstemmed Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title_short Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
title_sort exoskeleton follow-up control based on parameter optimization of predictive algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843196/
https://www.ncbi.nlm.nih.gov/pubmed/33552233
http://dx.doi.org/10.1155/2021/8850348
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