Inverse-Free Neurodynamic Approach With Self-Adaptive Gain for Time-Varying Quadratic Programming and Applications

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS
Ruiqi Zhou;Xingxing Ju;Qing Wang;Shan Jiang
{"title":"Inverse-Free Neurodynamic Approach With Self-Adaptive Gain for Time-Varying Quadratic Programming and Applications","authors":"Ruiqi Zhou;Xingxing Ju;Qing Wang;Shan Jiang","doi":"10.1109/LCSYS.2024.3449287","DOIUrl":null,"url":null,"abstract":"This letter proposes an inverse-free, noise-tolerant neurodynamic approach with a self-adaptive gain for solving time-varying quadratic programming problems (TVQPs). The proposed neurodynamic approach avoids inverting the coefficient matrix of TVQPs, resulting in lower computational complexity. It is demonstrated that the proposed approach ensures fixed-time convergence in noiseless conditions, and it achieves asymptotic convergence without requiring to anticipate the magnitudes of additive noises in noisy conditions. Additionally, the self-adaptive gain converges to a bounded constant rather than infinity in both noiseless and noisy scenarios. Simulation studies conducted on the redundant manipulator motion planning and ridge regression problem validate the effectiveness of the proposed approach.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Control Systems Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10646417/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This letter proposes an inverse-free, noise-tolerant neurodynamic approach with a self-adaptive gain for solving time-varying quadratic programming problems (TVQPs). The proposed neurodynamic approach avoids inverting the coefficient matrix of TVQPs, resulting in lower computational complexity. It is demonstrated that the proposed approach ensures fixed-time convergence in noiseless conditions, and it achieves asymptotic convergence without requiring to anticipate the magnitudes of additive noises in noisy conditions. Additionally, the self-adaptive gain converges to a bounded constant rather than infinity in both noiseless and noisy scenarios. Simulation studies conducted on the redundant manipulator motion planning and ridge regression problem validate the effectiveness of the proposed approach.
用于时变二次编程的具有自适应增益的无反神经动力学方法及其应用
这封信提出了一种无逆、容噪的神经动力学方法,该方法具有自适应增益,可用于求解时变二次编程问题(TVQPs)。所提出的神经动力学方法避免了对 TVQPs 的系数矩阵进行反演,从而降低了计算复杂度。研究表明,所提出的方法能确保在无噪声条件下的固定时间收敛,并能在噪声条件下实现渐近收敛,而无需预测加性噪声的大小。此外,无论是在无噪声还是有噪声的情况下,自适应增益都会收敛到一个有界常数,而不是无穷大。对冗余机械手运动规划和脊回归问题进行的仿真研究验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
×
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