Modeling continuous-time processes via input-to-state filters

被引：6
作者：
Mahata, Kaushik [1 ]
Fu, Minyue [1 ]
机构：
[1] Univ Newcastle, Sch Elect Engn & Comp Sci, Ctr Complex Dynam Syst & Control, Callaghan, NSW 2308, Australia
来源：
AUTOMATICA | 2006年 / 42卷 / 07期
关键词：
continuous-time processes; identification; input-to-state filtering; ARMA modeling;
D O I：
10.1016/j.automatica.2006.02.014
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1073 / 1084
页数：12
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