Ground Reaction Force Estimation in Robotic Prosthesis using Super-twisting Extended State Observer

Yongshan Huang, Hongxu Ma, Jin Zhang, Honglei An
{"title":"Ground Reaction Force Estimation in Robotic Prosthesis using Super-twisting Extended State Observer","authors":"Yongshan Huang, Hongxu Ma, Jin Zhang, Honglei An","doi":"10.1109/ICARM52023.2021.9536209","DOIUrl":null,"url":null,"abstract":"A ground reaction forces (GRFs) estimation method based on super-twisting extended state observer (STESO) for robotic prosthesis is proposed. The load cells and pressure sensors are not needed for the proposed method, and also the model of GRFs, hence it could adapt to different terrain environments. The GRFs estimate method uses a globally integral and super-twisting sliding model, which enables the observation error to converge to zero in finite time, and the GRFs estimator would not crash even if the initial estimation error is large. The stability and finite time convergence of the observer is rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the proposed GRFs estimates method.","PeriodicalId":367307,"journal":{"name":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARM52023.2021.9536209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

A ground reaction forces (GRFs) estimation method based on super-twisting extended state observer (STESO) for robotic prosthesis is proposed. The load cells and pressure sensors are not needed for the proposed method, and also the model of GRFs, hence it could adapt to different terrain environments. The GRFs estimate method uses a globally integral and super-twisting sliding model, which enables the observation error to converge to zero in finite time, and the GRFs estimator would not crash even if the initial estimation error is large. The stability and finite time convergence of the observer is rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the proposed GRFs estimates method.
基于超扭转扩展状态观测器的机器人假体地面反作用力估计
提出了一种基于超扭转扩展状态观测器(STESO)的机器人假肢地面反作用力估计方法。该方法不需要负载传感器和压力传感器,也不需要GRFs模型,因此可以适应不同的地形环境。GRFs估计方法采用全局积分和超扭转滑动模型,使观测误差在有限时间内收敛到零,即使初始估计误差很大,GRFs估计器也不会崩溃。对观测器的稳定性和有限时间收敛性进行了严格的证明和数学分析。仿真结果证明了该方法的可行性和有效性。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信