Hui Zhang, Jinghao Yan, Weiran Wang, Meng Xu, Wenjing Ma
{"title":"Self-tuning predictive control applicable to ship magnetic levitation damping device","authors":"Hui Zhang, Jinghao Yan, Weiran Wang, Meng Xu, Wenjing Ma","doi":"10.1049/cim2.12044","DOIUrl":null,"url":null,"abstract":"<p>In the ship design, there are strict vibration-proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self-tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self-tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least-squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti-interference performance obviously.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 1","pages":"58-70"},"PeriodicalIF":2.5000,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12044","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 1
Abstract
In the ship design, there are strict vibration-proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self-tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self-tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least-squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti-interference performance obviously.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).