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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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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. |
format | Online Article Text |
id | pubmed-7843196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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