增强的MATLAB lfr工具箱

S. Hecker, A. Varga, J. Magni
{"title":"增强的MATLAB lfr工具箱","authors":"S. Hecker, A. Varga, J. Magni","doi":"10.1109/CACSD.2004.1393845","DOIUrl":null,"url":null,"abstract":"We describe recent developments and enhancements of the LFR-toolbox for MATLAB for building LFT-based uncertainty models. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-models and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust FORTRAN implementations of order reduction tools via Mex-function interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-models with significantly lower orders","PeriodicalId":111199,"journal":{"name":"2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"86","resultStr":"{\"title\":\"Enhanced LFR-toolbox for MATLAB\",\"authors\":\"S. Hecker, A. Varga, J. Magni\",\"doi\":\"10.1109/CACSD.2004.1393845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe recent developments and enhancements of the LFR-toolbox for MATLAB for building LFT-based uncertainty models. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-models and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust FORTRAN implementations of order reduction tools via Mex-function interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-models with significantly lower orders\",\"PeriodicalId\":111199,\"journal\":{\"name\":\"2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"86\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CACSD.2004.1393845\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CACSD.2004.1393845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 86

摘要

我们描述了用于构建基于lft的不确定性模型的MATLAB lfr工具箱的最新发展和增强。一个主要的发展是新的lft对象定义支持大量的不确定性描述:连续和离散时间不确定性模型,正则和奇异参数表达式,更一般的不确定性块(非线性,时变等)。通过将名称与不确定性块关联,大大提高了生成的lft模型的可重用性和操作lfr描述的用户友好性。通过x函数接口,采用高效且数值鲁棒的FORTRAN实现降阶工具,显著提高了计算效率和数值精度。新的增强功能与改进的符号预处理相结合,通常会以更低的阶数更快地生成lft模型
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced LFR-toolbox for MATLAB
We describe recent developments and enhancements of the LFR-toolbox for MATLAB for building LFT-based uncertainty models. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-models and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust FORTRAN implementations of order reduction tools via Mex-function interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-models with significantly lower orders
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