基于CMAC的数据库驱动控制系统的实现

Zhifeng Li, Zhe Guan, Toru Yamamoto, S. Omatsu
{"title":"基于CMAC的数据库驱动控制系统的实现","authors":"Zhifeng Li, Zhe Guan, Toru Yamamoto, S. Omatsu","doi":"10.1109/ETFA45728.2021.9613413","DOIUrl":null,"url":null,"abstract":"As a nonlinear control algorithm, database-driven PID control (DD-PID) approach has been proposed to learn PID parameters based on a database. This method is based on a strategy in which PID parameters are determined based on neighboring data extracted based on the similarity between the query (current input/output data) and the information vector contained in the database. Since sorting operation is required in extracting the neighbor data, it is impossible to finish the calculation within a certain sampling interval for systems with fast response time, which is one of the hindrances in industrial applications. In addition, the DD-PID requires a large amount of storage memory in the database in order to obtain the desired control performance. On the other hand, one of the neural networks is the cerebellar model articulation controller (CMAC). It is a table-referenced adaptive learning controller. The major advantage of this method lies in the reduction of memory and computational load. This paper discusses a realization of the DD-PID by effectively utilizing the advantage of the CMAC.","PeriodicalId":312498,"journal":{"name":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Realization of a Database-Driven Control System Using a CMAC\",\"authors\":\"Zhifeng Li, Zhe Guan, Toru Yamamoto, S. Omatsu\",\"doi\":\"10.1109/ETFA45728.2021.9613413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a nonlinear control algorithm, database-driven PID control (DD-PID) approach has been proposed to learn PID parameters based on a database. This method is based on a strategy in which PID parameters are determined based on neighboring data extracted based on the similarity between the query (current input/output data) and the information vector contained in the database. Since sorting operation is required in extracting the neighbor data, it is impossible to finish the calculation within a certain sampling interval for systems with fast response time, which is one of the hindrances in industrial applications. In addition, the DD-PID requires a large amount of storage memory in the database in order to obtain the desired control performance. On the other hand, one of the neural networks is the cerebellar model articulation controller (CMAC). It is a table-referenced adaptive learning controller. The major advantage of this method lies in the reduction of memory and computational load. This paper discusses a realization of the DD-PID by effectively utilizing the advantage of the CMAC.\",\"PeriodicalId\":312498,\"journal\":{\"name\":\"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA45728.2021.9613413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA45728.2021.9613413","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

数据库驱动PID控制(DD-PID)是一种基于数据库学习PID参数的非线性控制算法。该方法基于一种策略,即根据查询(当前输入/输出数据)与数据库中包含的信息向量之间的相似性提取的相邻数据来确定PID参数。由于提取邻域数据需要进行排序操作,对于响应时间较快的系统,不可能在一定的采样间隔内完成计算,这是工业应用的障碍之一。此外,为了获得理想的控制性能,DD-PID需要在数据库中占用大量的存储内存。另一方面,其中一种神经网络是小脑模型关节控制器(CMAC)。它是一种表参考自适应学习控制器。该方法的主要优点在于减少了内存和计算量。本文讨论了一种有效利用CMAC的优势实现DD-PID的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Realization of a Database-Driven Control System Using a CMAC
As a nonlinear control algorithm, database-driven PID control (DD-PID) approach has been proposed to learn PID parameters based on a database. This method is based on a strategy in which PID parameters are determined based on neighboring data extracted based on the similarity between the query (current input/output data) and the information vector contained in the database. Since sorting operation is required in extracting the neighbor data, it is impossible to finish the calculation within a certain sampling interval for systems with fast response time, which is one of the hindrances in industrial applications. In addition, the DD-PID requires a large amount of storage memory in the database in order to obtain the desired control performance. On the other hand, one of the neural networks is the cerebellar model articulation controller (CMAC). It is a table-referenced adaptive learning controller. The major advantage of this method lies in the reduction of memory and computational load. This paper discusses a realization of the DD-PID by effectively utilizing the advantage of the CMAC.
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