E. Hasan, Rosdi bin Ibrahim, S. Ali, HassanSabo Miya, Syed Faizan Gilani
{"title":"U-Model based online identification of Air Flow Plant","authors":"E. Hasan, Rosdi bin Ibrahim, S. Ali, HassanSabo Miya, Syed Faizan Gilani","doi":"10.1109/ROMA.2016.7847809","DOIUrl":null,"url":null,"abstract":"A key and critical challenge in Industrial processes is real-time system identification. This has prompted a lot of research efforts towards the development of model based adaptive identification methods. Their key advantage is that system parameters are tuned adaptively and online. This paper proposes online identification of Air Flow Plant using adaptive U-Model. The recently developed model is based on a polynomial structure. It adaptively corresponds to uncertain system parameters to adjust them online. U-Model method has shown promising results in terms of system identification. The proposed method is verified by simulation. Being control oriented in nature, an effective control strategy based upon U-Model can easily be developed.","PeriodicalId":409977,"journal":{"name":"2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMA.2016.7847809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
A key and critical challenge in Industrial processes is real-time system identification. This has prompted a lot of research efforts towards the development of model based adaptive identification methods. Their key advantage is that system parameters are tuned adaptively and online. This paper proposes online identification of Air Flow Plant using adaptive U-Model. The recently developed model is based on a polynomial structure. It adaptively corresponds to uncertain system parameters to adjust them online. U-Model method has shown promising results in terms of system identification. The proposed method is verified by simulation. Being control oriented in nature, an effective control strategy based upon U-Model can easily be developed.