LMS自适应方法在降阶辨识时延估计中的应用

Pu Wang, Hongxin Li, Yancun Leng, Zhaohui Qiao
{"title":"LMS自适应方法在降阶辨识时延估计中的应用","authors":"Pu Wang, Hongxin Li, Yancun Leng, Zhaohui Qiao","doi":"10.1109/IHMSC.2014.169","DOIUrl":null,"url":null,"abstract":"The actual industrial systems are usually high order system with long time delay. These characteristics will bring a lot of troubles in building the models. Sometimes we need to know the pure delay of the system when designing the controller and at the same time we hope the model has low order. The traditional methods like unit step response and pade approximation to estimate time delay have some limitations. In this paper, we will use LMS(Least mean square) adaptive method to estimate time delay first and then use ARMAX model to reduced the order. The simulation has been conducted using the actual industrial data. The industrial system's order is very high even can reach 30 while we want to use this new method to reduce its order to about 10. At last, by comparing the performance index of 3 models, we prove this method can achieve the desired purpose.","PeriodicalId":370654,"journal":{"name":"2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Application of LMS Adaptive Method in Time Delay Estimation for Order Reduction Identification\",\"authors\":\"Pu Wang, Hongxin Li, Yancun Leng, Zhaohui Qiao\",\"doi\":\"10.1109/IHMSC.2014.169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The actual industrial systems are usually high order system with long time delay. These characteristics will bring a lot of troubles in building the models. Sometimes we need to know the pure delay of the system when designing the controller and at the same time we hope the model has low order. The traditional methods like unit step response and pade approximation to estimate time delay have some limitations. In this paper, we will use LMS(Least mean square) adaptive method to estimate time delay first and then use ARMAX model to reduced the order. The simulation has been conducted using the actual industrial data. The industrial system's order is very high even can reach 30 while we want to use this new method to reduce its order to about 10. At last, by comparing the performance index of 3 models, we prove this method can achieve the desired purpose.\",\"PeriodicalId\":370654,\"journal\":{\"name\":\"2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2014.169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2014.169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

实际的工业系统通常是长时延的高阶系统。这些特点会给模型的建立带来很多麻烦。有时我们在设计控制器时需要知道系统的纯延迟,同时希望模型是低阶的。传统的单位阶跃响应和页逼近法估计时延存在一定的局限性。在本文中,我们将首先使用LMS(最小均方)自适应方法估计时延,然后使用ARMAX模型降阶。利用实际工业数据进行了仿真。工业系统的订单非常高,甚至可以达到30,而我们想用这种新方法将其订单减少到10左右。最后,通过比较3种模型的性能指标,证明了该方法能够达到预期的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Application of LMS Adaptive Method in Time Delay Estimation for Order Reduction Identification
The actual industrial systems are usually high order system with long time delay. These characteristics will bring a lot of troubles in building the models. Sometimes we need to know the pure delay of the system when designing the controller and at the same time we hope the model has low order. The traditional methods like unit step response and pade approximation to estimate time delay have some limitations. In this paper, we will use LMS(Least mean square) adaptive method to estimate time delay first and then use ARMAX model to reduced the order. The simulation has been conducted using the actual industrial data. The industrial system's order is very high even can reach 30 while we want to use this new method to reduce its order to about 10. At last, by comparing the performance index of 3 models, we prove this method can achieve the desired purpose.
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