Model predictive control for upper limb rehabilitation robotic system under disturbed condition

S. F. Ahmed, Athar Ali, Syed Yarooq Raza, K. Kadir, M. K. Joyo, K. Naidu
{"title":"Model predictive control for upper limb rehabilitation robotic system under disturbed condition","authors":"S. F. Ahmed, Athar Ali, Syed Yarooq Raza, K. Kadir, M. K. Joyo, K. Naidu","doi":"10.1063/1.5118131","DOIUrl":null,"url":null,"abstract":"Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements. Nonlinear controllers make good option to this end as they adapt to handle the system uncertainties. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under various disturbed conditions. From the results MPC proves to be robust controller under the action of applied external disturbances.Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements. Nonlinear controllers make good option to this end as they adapt to handle the system uncertainties. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under various disturbed conditions. From the results MPC proves to be robust controller under the action of app...","PeriodicalId":112912,"journal":{"name":"APPLIED PHYSICS OF CONDENSED MATTER (APCOM 2019)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"APPLIED PHYSICS OF CONDENSED MATTER (APCOM 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/1.5118131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements. Nonlinear controllers make good option to this end as they adapt to handle the system uncertainties. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under various disturbed conditions. From the results MPC proves to be robust controller under the action of applied external disturbances.Demands for rehabilitation robots are now increasing day by day due to increase in the number of patients with neural disorder. These robots help the patients in therapeutic exercise performing specific movements which leads to mitigating neural disorders through a gradual improvement of the patients’ limb performances. As robots are the best suitable options to perform repetitive tasks without the risks of monotony and fatigue failure, rehabilitation via robots have proven to be more of a comfortable exercise than an exhausting treatment procedure. Rehabilitation robots require precise and efficient control in terms of position and force, ensuring thus accuracy in exercise movements. Nonlinear controllers make good option to this end as they adapt to handle the system uncertainties. This paper presents a Model Predictive Control (MPC) to control the rehabilitation robot for upper limb extremity under various disturbed conditions. From the results MPC proves to be robust controller under the action of app...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
扰动条件下上肢康复机器人系统的模型预测控制
由于神经系统疾病患者数量的增加,对康复机器人的需求日益增加。这些机器人帮助患者在治疗运动中进行特定的运动,通过逐渐改善患者的肢体表现来减轻神经障碍。由于机器人是执行重复性任务的最佳选择,没有单调和疲劳失败的风险,通过机器人进行康复已被证明是一种更舒适的锻炼,而不是令人筋疲力尽的治疗过程。康复机器人需要在位置和力度方面进行精确有效的控制,从而确保运动动作的准确性。非线性控制器可以很好地适应系统的不确定性。提出了一种基于模型预测控制的上肢康复机器人在各种干扰条件下的控制方法。结果表明,MPC控制器在外界扰动作用下具有鲁棒性。由于神经系统疾病患者数量的增加,对康复机器人的需求日益增加。这些机器人帮助患者在治疗运动中进行特定的运动,通过逐渐改善患者的肢体表现来减轻神经障碍。由于机器人是执行重复性任务的最佳选择,没有单调和疲劳失败的风险,通过机器人进行康复已被证明是一种更舒适的锻炼,而不是令人筋疲力尽的治疗过程。康复机器人需要在位置和力度方面进行精确有效的控制,从而确保运动动作的准确性。非线性控制器可以很好地适应系统的不确定性。提出了一种基于模型预测控制的上肢康复机器人在各种干扰条件下的控制方法。结果表明,MPC在应用程序的作用下是鲁棒控制器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Numerical investigation of the aerodynamic characteristics of a coupling of the three blades VAWT with a movable vanes Least square support vector machine technique for short term solar irradiance forecasting Thin film coating of copper nanoparticles with DC magnetron sputtering via physical vapor deposition Optimisation of scissor lifting machine structures using finite element analysis (FEA) Retina images classification using histogram of equivalent pattern (HEP) texture descriptors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1