{"title":"A Novel Mixture Least Squares Approach for Simultaneous Parameter/State and Unknown Input Estimation","authors":"Bo Ding;Yuanchu Wei;Yong Zhang;Wu Yang","doi":"10.1109/TAC.2024.3450001","DOIUrl":null,"url":null,"abstract":"In this article, we introduce a novel mixture least squares (MLSs) algorithm to deal with the problems of simultaneous parameter/state and unknown input estimation. First, the MLSs algorithm is derived to estimate the desired parameter and unknown input, which can be regarded as a unified framework for deterministic least squares and stochastic least squares. The unbiasedness and optimality of the MLSs estimators are further verified. Then, based on the established MLSs algorithm, a new solution to simultaneous state and unknown input estimation (SUIE) problems is given. The proposed method is more concise and straightforward than the existing SUIE algorithms. The method provided in this article offers fresh insight into parameter/state estimation with unknown input.","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"70 2","pages":"1252-1258"},"PeriodicalIF":7.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Automatic Control","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10648715/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, we introduce a novel mixture least squares (MLSs) algorithm to deal with the problems of simultaneous parameter/state and unknown input estimation. First, the MLSs algorithm is derived to estimate the desired parameter and unknown input, which can be regarded as a unified framework for deterministic least squares and stochastic least squares. The unbiasedness and optimality of the MLSs estimators are further verified. Then, based on the established MLSs algorithm, a new solution to simultaneous state and unknown input estimation (SUIE) problems is given. The proposed method is more concise and straightforward than the existing SUIE algorithms. The method provided in this article offers fresh insight into parameter/state estimation with unknown input.
期刊介绍：
In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering. Two types of contributions are regularly considered:
1) Papers: Presentation of significant research, development, or application of control concepts.
2) Technical Notes and Correspondence: Brief technical notes, comments on published areas or established control topics, corrections to papers and notes published in the Transactions.
In addition, special papers (tutorials, surveys, and perspectives on the theory and applications of control systems topics) are solicited.
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