具有保证稳定性的神经网络机器人控制器

F. Lewis, A. Yesildirek, K. Liu
{"title":"具有保证稳定性的神经网络机器人控制器","authors":"F. Lewis, A. Yesildirek, K. Liu","doi":"10.1109/IFIS.1993.324205","DOIUrl":null,"url":null,"abstract":"A multilayer neural net (NN) controller for a general serial-link robot arm is developed. The structure of the NN controller is derived using a filtered error approach. No learning phase is needed. It is argued that standard backpropagation tuning, when used for real-time closed-loop control, can yield unbounded NN weights if: (1) the net cannot exactly reconstruct a certain required nonlinear control function; (2) there are bounded unknown disturbances in the robot dynamics; or (3) the robot arm has more than one link (i.e. nonlinear case). Novel online weight tuning algorithms given include correction terms to backpropagation, plus an added robustifying signal, and guarantee tracking as well as bounded weights.<<ETX>>","PeriodicalId":408138,"journal":{"name":"Third International Conference on Industrial Fuzzy Control and Intelligent Systems","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Neural net robot controller with guaranteed stability\",\"authors\":\"F. Lewis, A. Yesildirek, K. Liu\",\"doi\":\"10.1109/IFIS.1993.324205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multilayer neural net (NN) controller for a general serial-link robot arm is developed. The structure of the NN controller is derived using a filtered error approach. No learning phase is needed. It is argued that standard backpropagation tuning, when used for real-time closed-loop control, can yield unbounded NN weights if: (1) the net cannot exactly reconstruct a certain required nonlinear control function; (2) there are bounded unknown disturbances in the robot dynamics; or (3) the robot arm has more than one link (i.e. nonlinear case). Novel online weight tuning algorithms given include correction terms to backpropagation, plus an added robustifying signal, and guarantee tracking as well as bounded weights.<<ETX>>\",\"PeriodicalId\":408138,\"journal\":{\"name\":\"Third International Conference on Industrial Fuzzy Control and Intelligent Systems\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Industrial Fuzzy Control and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IFIS.1993.324205\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Industrial Fuzzy Control and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFIS.1993.324205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

针对通用串行连杆机械臂,设计了一种多层神经网络控制器。采用误差滤波方法推导了神经网络控制器的结构。不需要学习阶段。本文认为,当标准反向传播调谐用于实时闭环控制时,如果:(1)网络不能精确地重建某个所需的非线性控制函数,则会产生无界的NN权值;(2)机器人动力学中存在有界未知扰动;或者(3)机械臂有多个连杆(即非线性情况)。给出了一种新的在线权值调整算法,该算法包括反向传播的校正项,加上一个附加的鲁棒信号,并保证了跟踪和有界权值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural net robot controller with guaranteed stability
A multilayer neural net (NN) controller for a general serial-link robot arm is developed. The structure of the NN controller is derived using a filtered error approach. No learning phase is needed. It is argued that standard backpropagation tuning, when used for real-time closed-loop control, can yield unbounded NN weights if: (1) the net cannot exactly reconstruct a certain required nonlinear control function; (2) there are bounded unknown disturbances in the robot dynamics; or (3) the robot arm has more than one link (i.e. nonlinear case). Novel online weight tuning algorithms given include correction terms to backpropagation, plus an added robustifying signal, and guarantee tracking as well as bounded weights.<>
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:604180095
Book学术官方微信