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Interaction learning control with movement primitives for lower limb exoskeleton
Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human–robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807752/ https://www.ncbi.nlm.nih.gov/pubmed/36605521 http://dx.doi.org/10.3389/fnbot.2022.1086578 |
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author | Wang, Jiaqi Wu, Dongmei Gao, Yongzhuo Dong, Wei |
author_facet | Wang, Jiaqi Wu, Dongmei Gao, Yongzhuo Dong, Wei |
author_sort | Wang, Jiaqi |
collection | PubMed |
description | Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human–robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's intention. This paper proposed a novel interaction learning control strategy for the lower extremity exoskeleton. A powerful representative tool probabilistic movement primitives (ProMPs) is adopted to model the motion and generate the desired trajectory in real-time. To adjust the trajectory by the user's real-time intention, a compensation term based on human–robot interaction force is designed and merged into the ProMPs model. Then, compliant impedance control is adopted as a low-level control where the desired trajectory is put into. Moreover, the model will be dynamically adapted online by penalizing both the interaction force and trajectory mismatch, with all the parameters that can be further learned by learning algorithm PI(BB). The experimental results verified the effectiveness of the proposed control framework. |
format | Online Article Text |
id | pubmed-9807752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98077522023-01-04 Interaction learning control with movement primitives for lower limb exoskeleton Wang, Jiaqi Wu, Dongmei Gao, Yongzhuo Dong, Wei Front Neurorobot Neuroscience Research on robotic exoskeletons both in the military and medical fields has rapidly expanded over the previous decade. As a human–robot interaction system, it is a challenge to develop an assistive strategy that makes the exoskeleton supply efficient and natural assistance following the user's intention. This paper proposed a novel interaction learning control strategy for the lower extremity exoskeleton. A powerful representative tool probabilistic movement primitives (ProMPs) is adopted to model the motion and generate the desired trajectory in real-time. To adjust the trajectory by the user's real-time intention, a compensation term based on human–robot interaction force is designed and merged into the ProMPs model. Then, compliant impedance control is adopted as a low-level control where the desired trajectory is put into. Moreover, the model will be dynamically adapted online by penalizing both the interaction force and trajectory mismatch, with all the parameters that can be further learned by learning algorithm PI(BB). The experimental results verified the effectiveness of the proposed control framework. Frontiers Media S.A. 2022-12-20 /pmc/articles/PMC9807752/ /pubmed/36605521 http://dx.doi.org/10.3389/fnbot.2022.1086578 Text en Copyright © 2022 Wang, Wu, Gao and Dong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wang, Jiaqi Wu, Dongmei Gao, Yongzhuo Dong, Wei Interaction learning control with movement primitives for lower limb exoskeleton |
title | Interaction learning control with movement primitives for lower limb exoskeleton |
title_full | Interaction learning control with movement primitives for lower limb exoskeleton |
title_fullStr | Interaction learning control with movement primitives for lower limb exoskeleton |
title_full_unstemmed | Interaction learning control with movement primitives for lower limb exoskeleton |
title_short | Interaction learning control with movement primitives for lower limb exoskeleton |
title_sort | interaction learning control with movement primitives for lower limb exoskeleton |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807752/ https://www.ncbi.nlm.nih.gov/pubmed/36605521 http://dx.doi.org/10.3389/fnbot.2022.1086578 |
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