文章基本信息

标题：Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems
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作者：Mohammad Rashedi ; Jinfeng Liu ; Biao Huang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：8
页码：158-163
DOI：10.1016/j.ifacol.2015.08.174
语种：English
出版社：Elsevier
摘要：AbstractIn this work, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters communicate with each other to exchange subsystem state estimates. First, an implementation strategy which specifies how the distributed filters should communicate is designed. Second, the detailed design of the subsystem filter is described. Subsequently, the stability of the proposed distributed state estimation is analyzed. Finally, the effectiveness and applicability of the proposed design are illustrated via the application to a chemical process example.
关键词：KeywordsDistributed state estimationExtended Kalman filterNonlinear systemsHigh gain observer