一种多通道主动噪声控制系统,采用基于深度学习的方法估计次要路径,并采用归一化聚类控制策略来控制车内发动机噪声

IF 3.4 2区 物理与天体物理 Q1 ACOUSTICS Applied Acoustics Pub Date : 2024-09-11 DOI:10.1016/j.apacoust.2024.110263
{"title":"一种多通道主动噪声控制系统,采用基于深度学习的方法估计次要路径,并采用归一化聚类控制策略来控制车内发动机噪声","authors":"","doi":"10.1016/j.apacoust.2024.110263","DOIUrl":null,"url":null,"abstract":"<div><p>Although the multi-channel active noise control (ANC) system based on the traditional clustered control strategy solves the problems of high algorithm complexity and fragile stability, the step size setting of each local controller relies on the trial-and-error method, which makes it difficult to trade-off the convergence speed and the steady-state error of the algorithm, and the secondary path estimation is susceptible to the interference of the dynamic environment. To solve the above problems, this study proposes an efficient multi-channel ANC system, which accurately estimates the secondary paths based on the deep learning prediction model and adopts a normalized-clustered control strategy to normalize the step size of the two-channel FxLMS algorithm of each local controller, which balances the system convergence speed and the steady-state error. A series of real-vehicle ANC experiments are conducted. The results show that under stationary conditions, the proposed control strategy control converges quickly and has better noise reduction performance than the traditional clustered control strategy. Under non-stationary conditions, after normalizing the step size of the local controller, the proposed control strategy can better balance the steady-state error and the convergence speed of the control strategy and improve the noise reduction tracking ability. Finally, it is verified that the proposed deep learning method can accurately estimate the secondary path after changes and ensure the noise reduction performance of the proposed control strategy.</p></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-channel active noise control system using deep learning-based method to estimate secondary path and normalized-clustered control strategy for vehicle interior engine noise\",\"authors\":\"\",\"doi\":\"10.1016/j.apacoust.2024.110263\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Although the multi-channel active noise control (ANC) system based on the traditional clustered control strategy solves the problems of high algorithm complexity and fragile stability, the step size setting of each local controller relies on the trial-and-error method, which makes it difficult to trade-off the convergence speed and the steady-state error of the algorithm, and the secondary path estimation is susceptible to the interference of the dynamic environment. To solve the above problems, this study proposes an efficient multi-channel ANC system, which accurately estimates the secondary paths based on the deep learning prediction model and adopts a normalized-clustered control strategy to normalize the step size of the two-channel FxLMS algorithm of each local controller, which balances the system convergence speed and the steady-state error. A series of real-vehicle ANC experiments are conducted. The results show that under stationary conditions, the proposed control strategy control converges quickly and has better noise reduction performance than the traditional clustered control strategy. Under non-stationary conditions, after normalizing the step size of the local controller, the proposed control strategy can better balance the steady-state error and the convergence speed of the control strategy and improve the noise reduction tracking ability. Finally, it is verified that the proposed deep learning method can accurately estimate the secondary path after changes and ensure the noise reduction performance of the proposed control strategy.</p></div>\",\"PeriodicalId\":55506,\"journal\":{\"name\":\"Applied Acoustics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Acoustics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003682X24004146\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X24004146","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0

摘要

基于传统聚类控制策略的多通道主动噪声控制(ANC)系统虽然解决了算法复杂度高、稳定性脆弱等问题,但各局部控制器的步长设置依赖于试错法,难以权衡算法的收敛速度和稳态误差,且二次路径估计易受动态环境的干扰。为解决上述问题,本研究提出了一种高效的多通道 ANC 系统,该系统基于深度学习预测模型精确估计二次路径,并采用归一化聚类控制策略对各局部控制器的双通道 FxLMS 算法步长进行归一化处理,平衡了系统收敛速度和稳态误差。研究人员进行了一系列实车 ANC 实验。结果表明,在静态条件下,所提出的控制策略控制收敛速度快,降噪性能优于传统的集群控制策略。在非稳态条件下,对局部控制器的步长进行归一化处理后,所提出的控制策略能更好地平衡稳态误差和控制策略的收敛速度,提高降噪跟踪能力。最后,验证了所提出的深度学习方法能够准确估计变化后的次级路径,保证了所提出控制策略的降噪性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A multi-channel active noise control system using deep learning-based method to estimate secondary path and normalized-clustered control strategy for vehicle interior engine noise

Although the multi-channel active noise control (ANC) system based on the traditional clustered control strategy solves the problems of high algorithm complexity and fragile stability, the step size setting of each local controller relies on the trial-and-error method, which makes it difficult to trade-off the convergence speed and the steady-state error of the algorithm, and the secondary path estimation is susceptible to the interference of the dynamic environment. To solve the above problems, this study proposes an efficient multi-channel ANC system, which accurately estimates the secondary paths based on the deep learning prediction model and adopts a normalized-clustered control strategy to normalize the step size of the two-channel FxLMS algorithm of each local controller, which balances the system convergence speed and the steady-state error. A series of real-vehicle ANC experiments are conducted. The results show that under stationary conditions, the proposed control strategy control converges quickly and has better noise reduction performance than the traditional clustered control strategy. Under non-stationary conditions, after normalizing the step size of the local controller, the proposed control strategy can better balance the steady-state error and the convergence speed of the control strategy and improve the noise reduction tracking ability. Finally, it is verified that the proposed deep learning method can accurately estimate the secondary path after changes and ensure the noise reduction performance of the proposed control strategy.

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
Applied Acoustics
Applied Acoustics 物理-声学
CiteScore
7.40
自引率
11.80%
发文量
618
审稿时长
7.5 months
期刊介绍: Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense. Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems. Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.
期刊最新文献
Motion coprime array-based DOA estimation considering phase disturbance of sensor array Prediction of flanking sound transmission through cross-laminated timber junctions with resilient interlayers TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals Voice handicap prevalence among healthcare workers in China and Indonesia Acoustic metaslit for regional sound insulation for a three-dimensional diffuse sound field incidence
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1