文章基本信息

标题：Development of iterative extended Kalman filter for DAE System
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作者：Jalesh L. Purohit ; Sachin C. Patwardhan
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：1
页码：691-696
DOI：10.1016/j.ifacol.2018.05.116
语种：English
出版社：Elsevier
摘要：AbstractExtended Kalman filtering (EKF) is widely used for estimating states of systems modelled as ordinary differential equations (ODEs). The performance of this approach may deteriorate if the initial estimate is poor i.e. far from true state. Iterative version of EKF developed by Wishner et al. (1969) for systems modelled as ODEs can alleviate this difficulty with negligible additional computational efforts as it involves smoothing of the prior estimate. There are many systems that involve different time scales and are described by Differential-Algebraic equations (DAEs). Extension of Bayesian state estimation approach such as EKF for handling nonlinear DAEs is relatively recent development (Beccera et al. (2001) and Mandela et al. (2010)). In this work, taking motivation from Wishner et al. (1969), an iterative EKF (IEKF) scheme is proposed for systems modelled as DAEs. The efficacy of the proposed DAE-IEKF is evaluated using simulation studies of Nickel-Hydrogen electrode system and divided wall distillation column system. Analysis of the simulation results reveals that the proposed approach results in a reasonable improvement in the estimation performance when compared with performance of the conventional DAE-EKF.
关键词：KeywordsExtended Kalman Filter (EKF)Differential - Algebraic Equation Systems (DAEs)Nonlinear State Estimation