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An Advanced Adaptive Control of Lower Limb Rehabilitation Robot

Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in t...

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Autores principales: Du, Yihao, Wang, Hao, Qiu, Shi, Yao, Wenxuan, Xie, Ping, Chen, Xiaoling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805759/
https://www.ncbi.nlm.nih.gov/pubmed/33500995
http://dx.doi.org/10.3389/frobt.2018.00116
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author Du, Yihao
Wang, Hao
Qiu, Shi
Yao, Wenxuan
Xie, Ping
Chen, Xiaoling
author_facet Du, Yihao
Wang, Hao
Qiu, Shi
Yao, Wenxuan
Xie, Ping
Chen, Xiaoling
author_sort Du, Yihao
collection PubMed
description Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field.
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spelling pubmed-78057592021-01-25 An Advanced Adaptive Control of Lower Limb Rehabilitation Robot Du, Yihao Wang, Hao Qiu, Shi Yao, Wenxuan Xie, Ping Chen, Xiaoling Front Robot AI Robotics and AI Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field. Frontiers Media S.A. 2018-10-08 /pmc/articles/PMC7805759/ /pubmed/33500995 http://dx.doi.org/10.3389/frobt.2018.00116 Text en Copyright © 2018 Du, Wang, Qiu, Yao, Xie and Chen. http://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 Robotics and AI
Du, Yihao
Wang, Hao
Qiu, Shi
Yao, Wenxuan
Xie, Ping
Chen, Xiaoling
An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title_full An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title_fullStr An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title_full_unstemmed An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title_short An Advanced Adaptive Control of Lower Limb Rehabilitation Robot
title_sort advanced adaptive control of lower limb rehabilitation robot
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805759/
https://www.ncbi.nlm.nih.gov/pubmed/33500995
http://dx.doi.org/10.3389/frobt.2018.00116
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