Parameters identification of robot manipulator based on particle swarm optimization

N. Mizuno, Canh Son Nguyen
{"title":"Parameters identification of robot manipulator based on particle swarm optimization","authors":"N. Mizuno, Canh Son Nguyen","doi":"10.1109/ICCA.2017.8003078","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate identification methods for dynamic parameters of robot manipulator. The focused method is based on heuristic particle swarm optimization algorithm (PSO) with some extended features. The estimated parameters by PSO are used to predict required joint torques for high accuracy tracking control. The effectiveness of some PSO methods for tracking control problem are verified by cross-validation with data set produced by several trajectories.","PeriodicalId":379025,"journal":{"name":"2017 13th IEEE International Conference on Control & Automation (ICCA)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE International Conference on Control & Automation (ICCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCA.2017.8003078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

In this paper, we investigate identification methods for dynamic parameters of robot manipulator. The focused method is based on heuristic particle swarm optimization algorithm (PSO) with some extended features. The estimated parameters by PSO are used to predict required joint torques for high accuracy tracking control. The effectiveness of some PSO methods for tracking control problem are verified by cross-validation with data set produced by several trajectories.
基于粒子群优化的机械臂参数辨识
本文研究了机器人机械臂动态参数的辨识方法。该方法基于启发式粒子群优化算法(PSO),并具有一些扩展特征。利用粒子群算法估计的参数预测关节所需的力矩,实现高精度跟踪控制。通过对多个轨迹产生的数据集进行交叉验证,验证了粒子群算法在跟踪控制问题中的有效性。
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:604180095
Book学术官方微信