A magnitude optimum approach for tuning Reduced-order ADRC with FOPDT models

M. Srikanth, N. Yadaiah
{"title":"A magnitude optimum approach for tuning Reduced-order ADRC with FOPDT models","authors":"M. Srikanth, N. Yadaiah","doi":"10.1109/ICC54714.2021.9703133","DOIUrl":null,"url":null,"abstract":"In this paper, the Reduced-order Active Disturbance Rejection Control (RADRC) is tuned with a new set of tuning rules based on the First Order Plus Dead-time plant models. The tuning rules are developed to achieve the desired robustness $(M_{s})$ level. The tuning process is carried out in two stages. In the first stage, a set of non-linear equations is formulated using the magnitude optimum method and are solved with the desired settling time requirement resulting in controller bandwidth $(\\omega_{c})$, observer bandwidth $(\\omega_{0})$ and high-frequency gain $(b_{0})$. The parameter $b_{0}$ is further adjusted to meet the robustness $(M_{s})$ and stability requirements. The data collected from stage-I is used as input to the next stage. In stage-II, tuning rules for $\\omega_{0}$ and $b_{0}$ are formulated in the form of a polynomial model. Finally, the proposed tuning rules are tested on standard benchmark systems and experimentally verified to control a DC motor.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"55 30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Seventh Indian Control Conference (ICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICC54714.2021.9703133","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 paper, the Reduced-order Active Disturbance Rejection Control (RADRC) is tuned with a new set of tuning rules based on the First Order Plus Dead-time plant models. The tuning rules are developed to achieve the desired robustness $(M_{s})$ level. The tuning process is carried out in two stages. In the first stage, a set of non-linear equations is formulated using the magnitude optimum method and are solved with the desired settling time requirement resulting in controller bandwidth $(\omega_{c})$, observer bandwidth $(\omega_{0})$ and high-frequency gain $(b_{0})$. The parameter $b_{0}$ is further adjusted to meet the robustness $(M_{s})$ and stability requirements. The data collected from stage-I is used as input to the next stage. In stage-II, tuning rules for $\omega_{0}$ and $b_{0}$ are formulated in the form of a polynomial model. Finally, the proposed tuning rules are tested on standard benchmark systems and experimentally verified to control a DC motor.
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
基于FOPDT模型的降阶自抗扰控制器调优方法
本文基于一阶加死区时间对象模型,采用一套新的自抗扰规则对降阶自抗扰控制进行了整定。开发了调优规则以达到期望的鲁棒性$(M_{s})$水平。调整过程分两个阶段进行。在第一阶段,使用幅度优化方法建立一组非线性方程,并根据所需的沉降时间要求进行求解,得到控制器带宽$(\omega_{c})$,观测器带宽$(\omega_{0})$和高频增益$(b_{0})$。进一步调整参数$b_{0}$以满足鲁棒性$(M_{s})$和稳定性要求。从第一阶段收集的数据用作下一阶段的输入。在阶段ii中,$\omega_{0}$和$b_{0}$的调谐规则以多项式模型的形式表示。最后,在标准基准系统上对所提出的整定规则进行了测试,并对直流电机的控制效果进行了实验验证。
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
Robust Control of Buck-Boost Converter using Second Order Sliding Modes Finite-Time Stability Analysis of a Distributed Microgrid Connected via Detail-Balanced Graph Improving network's transition cohesion by approximating strongly damped waves using delayed self reinforcement Nonlinear Spacecraft Attitude Control Design Using Modified Rodrigues Parameters Comparison of Deep Reinforcement Learning Techniques with Gradient based approach in Cooperative Control of Wind Farm
×
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