文章基本信息

标题：Unconstrained Nonlinear State Estimation for Tennessee Eastman Challenge Process
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作者：Krishna Kumar Kottakki ; Mani Bhushan ; Sharad Bhartiya 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：12919-12924
DOI：10.1016/j.ifacol.2017.08.1788
语种：English
出版社：Elsevier
摘要：AbstractTennessee Eastman challenge process (TE) has been well known in process control literature as a challenge problem in estimation and control. Complexities in this nonlinear process and corresponding measurement models lead to non-Gaussian densities and estimators which can accommodate the non-Gaussianity can give better state estimates. In this work, we compare and demonstrate the utility of recently developed Sum of Gaussians based Unscented Gaussian Sum Filter (UGSF) for performing nonlinear state estimation of the process. UGSF uses sigma point concept to capture process nonlinearities coupled with a Sum of Gaussians approximation to achieve an accurate representation of non-Gaussian prior densities evolved from the process. The performance of UGSF is compared with the well known Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) approaches, for one of the operating modes of TE process. Results demonstrate the utility of using UGSF as a state estimator for the large-dimensional and highly nonlinear TE process.
关键词：KeywordsTennessee Eastman processSum of GaussiansUnscented transformationKalman Filter