多自由度机械臂轨迹跟踪控制研究

L. Shaoming, L. Ruipeng
{"title":"多自由度机械臂轨迹跟踪控制研究","authors":"L. Shaoming, L. Ruipeng","doi":"10.1109/YAC.2017.7967408","DOIUrl":null,"url":null,"abstract":"According to the multiple degree of freedom manipulator trajectory tracking instability in a timely manner, the paper uses the fuzzy neural network algorithm of trajectory tracking control. Fuzzy neural network control algorithm combining the advantages of the two algorithms effectively, do not rely on multiple DOF mechanical arm precision model structure, which can be directly used to control the amount of self-learning by adjusting mechanical arm joint, and then determine the structure and parameters of control, very suitable for the control of multiple degree of freedom mechanical arm. The analysis and simulation studies show that the fuzzy neural network controller can track the multiple DOF mechanical arm trajectory control system has strong adaptability and robustness.","PeriodicalId":232358,"journal":{"name":"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"56 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Research on trajectory tracking control of multiple degree of freedom manipulator\",\"authors\":\"L. Shaoming, L. Ruipeng\",\"doi\":\"10.1109/YAC.2017.7967408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to the multiple degree of freedom manipulator trajectory tracking instability in a timely manner, the paper uses the fuzzy neural network algorithm of trajectory tracking control. Fuzzy neural network control algorithm combining the advantages of the two algorithms effectively, do not rely on multiple DOF mechanical arm precision model structure, which can be directly used to control the amount of self-learning by adjusting mechanical arm joint, and then determine the structure and parameters of control, very suitable for the control of multiple degree of freedom mechanical arm. The analysis and simulation studies show that the fuzzy neural network controller can track the multiple DOF mechanical arm trajectory control system has strong adaptability and robustness.\",\"PeriodicalId\":232358,\"journal\":{\"name\":\"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"volume\":\"56 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YAC.2017.7967408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YAC.2017.7967408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

针对多自由度机械臂轨迹跟踪不稳定的情况,本文采用模糊神经网络算法进行轨迹跟踪控制。模糊神经网络控制算法有效地结合了两种算法的优点,不依赖于多自由度机械臂的精密模型结构,可以直接通过调节机械臂关节的自学习量来控制,然后确定控制结构和参数,非常适合多自由度机械臂的控制。分析和仿真研究表明,模糊神经网络控制器能够跟踪多自由度机械臂轨迹控制系统,具有较强的适应性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on trajectory tracking control of multiple degree of freedom manipulator
According to the multiple degree of freedom manipulator trajectory tracking instability in a timely manner, the paper uses the fuzzy neural network algorithm of trajectory tracking control. Fuzzy neural network control algorithm combining the advantages of the two algorithms effectively, do not rely on multiple DOF mechanical arm precision model structure, which can be directly used to control the amount of self-learning by adjusting mechanical arm joint, and then determine the structure and parameters of control, very suitable for the control of multiple degree of freedom mechanical arm. The analysis and simulation studies show that the fuzzy neural network controller can track the multiple DOF mechanical arm trajectory control system has strong adaptability and robustness.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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