Constancy Investigation Of Fuzzy Controller Over Proportional Integral Controller For Resonant Converter

N. Madhanakkumar, Arulkumar Subramanian, M. Vijayaragavan, V. Sriramkumar
{"title":"Constancy Investigation Of Fuzzy Controller Over Proportional Integral Controller For Resonant Converter","authors":"N. Madhanakkumar, Arulkumar Subramanian, M. Vijayaragavan, V. Sriramkumar","doi":"10.1109/ICCCI56745.2023.10128176","DOIUrl":null,"url":null,"abstract":"In this article mainly aimed to reduce the disturbance raised in the line or load side of resonant converter. We have to achieve effective output from the resonant converter among any disturbance condition. So that the previous literature survey the resonant converter is subjected to line and load side disturbances. In the survey told that the resonant converter works along with the help of controllers will give the better output when compared with the resonant converter working alone. Here the designed resonant converter is manually subjected to line and load disturbances with input voltage of 55V supplying for 80ohms load which the converter is integrating with Proportional Integral (PI) and Fuzzy logic controller. The designed converter’s disturbance output voltages and currents waveforms of PI and Fuzzy controllers are compared by time domain specifications like peak time, raise time, settling time and peak overshoot are measured by using output waveforms and then we have to conclude that the recital working of Fuzzy controller is more effective than the Proportional Integral (PI) controller.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Computer Communication and Informatics (ICCCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCI56745.2023.10128176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this article mainly aimed to reduce the disturbance raised in the line or load side of resonant converter. We have to achieve effective output from the resonant converter among any disturbance condition. So that the previous literature survey the resonant converter is subjected to line and load side disturbances. In the survey told that the resonant converter works along with the help of controllers will give the better output when compared with the resonant converter working alone. Here the designed resonant converter is manually subjected to line and load disturbances with input voltage of 55V supplying for 80ohms load which the converter is integrating with Proportional Integral (PI) and Fuzzy logic controller. The designed converter’s disturbance output voltages and currents waveforms of PI and Fuzzy controllers are compared by time domain specifications like peak time, raise time, settling time and peak overshoot are measured by using output waveforms and then we have to conclude that the recital working of Fuzzy controller is more effective than the Proportional Integral (PI) controller.
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
谐振变换器模糊控制器优于比例积分控制器的恒常性研究
本文主要针对谐振变换器的线路侧或负载侧产生的扰动进行减小。我们必须在任何干扰条件下都能实现谐振变换器的有效输出。因此,以前的文献研究了谐振变换器受到线侧和负载侧干扰的情况。在调查中告诉我们,谐振变换器在控制器的帮助下协同工作比谐振变换器单独工作具有更好的输出。本文设计的谐振变换器以55V的输入电压为80欧姆的负载供电,通过比例积分(PI)和模糊控制器对其进行积分,人为地对线路和负载进行扰动。通过测量输出波形的峰值时间、上升时间、稳定时间和峰值超调量等时域指标,比较了PI控制器和模糊控制器所设计的变换器的扰动输出电压和电流波形,得出模糊控制器的输出工作比比例积分(PI)控制器更有效的结论。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysis of Cloud Computing Security Challenges and Threats for Resolving Data Breach Issues Parkinson’s disease classification using Machine Learning techniques An Autonomous Crop-Cutting Mechanism Using A Drone Extensive Review on Predicting Heart Disease Using Machine Learning and Deep Learning Techniques Chest Disease Classification Using Convolutional Neural Networks
×
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