文章基本信息

标题：Robust State Estimations in Controlled ARMA Processes with the Non-Gaussian Noises: Applications to the Delayed Dynamics
本地全文：下载
作者：Vadim Azhmyakov ; Jose Perea Arango ; Moises Bonilla 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：20
页码：334-339
DOI：10.1016/j.ifacol.2021.11.196
语种：English
出版社：Elsevier
摘要：AbstractThis paper deals with a novel theoretic approach to the robust state estimations in discrete-time dynamic systems with the non-Gaussian correlated stochastic noises. The methodology we develop is based on the so-called ”worst case” robust Kalman Filter (KF) approach proposed in [2,3]. We are interested in the robust state estimation for the controlled ARMA models under assumption of the colored noises. Since an ARMA model involves the correlated noises in the equivalent Linear Model (LM) representation, the resulting dynamic system also includes the correlated stochastic variables. These two crucial properties of the ARMA models under consideration imply the impossibility of application of the classic KF-type state estimations. We use the modified ”instrumental variable” method and derive an auxiliary LM with the uncorrelated noises. Application of the robust KF to this auxiliary LM makes it possible to derive a guaranteed state estimation in the initially given ARMA model. The proposed non-standard KF based state estimations are finally applied to the linear stochastic dynamic systems with the time delays.