{"title":"Prescribed-time observer for descriptor systems with unknown input","authors":"Jiancheng Zhang , Yongduan Song , Gang Zheng","doi":"10.1016/j.automatica.2024.111999","DOIUrl":null,"url":null,"abstract":"<div><div>This paper investigates the problem of simultaneous state and unknown input estimation/observation within a prescribed time for a class of descriptor systems with unknown inputs (UIs). Firstly, for the descriptor system with UIs, a new structure decomposition is developed which provides a more straightforward way to estimate the state and UIs. Subsequently, based on the sub-system obtained by the decomposition, new prescribed time unknown input observers (PTUIOs) are developed for both the single-output and the multiple-output cases, which, on the one hand, achieves the simultaneous state and UI estimation within an arbitrarily prescribed time, and on the other hand, breaks through the restriction imposed in the literature that the sub-system should be single-output and meanwhile be in a special canonical form. The necessary and sufficient conditions for the existence of the PTUIO are established in terms of the original system matrices. Finally, two examples are presented to verify the effectiveness of the proposed method.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"172 ","pages":"Article 111999"},"PeriodicalIF":4.8000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S000510982400493X","RegionNum":2,"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
This paper investigates the problem of simultaneous state and unknown input estimation/observation within a prescribed time for a class of descriptor systems with unknown inputs (UIs). Firstly, for the descriptor system with UIs, a new structure decomposition is developed which provides a more straightforward way to estimate the state and UIs. Subsequently, based on the sub-system obtained by the decomposition, new prescribed time unknown input observers (PTUIOs) are developed for both the single-output and the multiple-output cases, which, on the one hand, achieves the simultaneous state and UI estimation within an arbitrarily prescribed time, and on the other hand, breaks through the restriction imposed in the literature that the sub-system should be single-output and meanwhile be in a special canonical form. The necessary and sufficient conditions for the existence of the PTUIO are established in terms of the original system matrices. Finally, two examples are presented to verify the effectiveness of the proposed method.
期刊介绍:
Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field.
After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience.
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