{"title":"Receding Horizon Estimation for Networked Control Systems with Packet Losses","authors":"Chaochao Li, Chunyan Han, Fang He","doi":"10.1109/ICARCV.2018.8581105","DOIUrl":null,"url":null,"abstract":"This paper is interested in the receding horizon estimation problem for the networked control systems with packet losses. Different from the scalar data losses case, we introduce a diagonal matrix to represent the phenomenon of packet losses, where each element of the diagonal matrix is a binary stochastic variable and indicates the arrival of the corresponding measurement component. That means different observation components have different packet loss probability. A batch form and a recursive form for the receding horizon estimation are proposed by minimizing a new cost function that includes two terminal weighting terms. Using the derived condition, the stability of the proposed receding horizon estimation is proved. Finally, a numerical example is provided for illustration.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2018.8581105","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
This paper is interested in the receding horizon estimation problem for the networked control systems with packet losses. Different from the scalar data losses case, we introduce a diagonal matrix to represent the phenomenon of packet losses, where each element of the diagonal matrix is a binary stochastic variable and indicates the arrival of the corresponding measurement component. That means different observation components have different packet loss probability. A batch form and a recursive form for the receding horizon estimation are proposed by minimizing a new cost function that includes two terminal weighting terms. Using the derived condition, the stability of the proposed receding horizon estimation is proved. Finally, a numerical example is provided for illustration.