Filtering-based recursive least-squares identification algorithm for controlled autoregressive moving average systems using the maximum likelihood principle

被引：10

作者：

Li, Junhong ^{[1
,2
]}

Ding, Feng ^{[2
]}

机构：

[1] Nantong Univ, Sch Elect Engn, Nantong, Jiangsu, Peoples R China

[2] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

来源：

JOURNAL OF VIBRATION AND CONTROL | 2015年 / 21卷 / 15期

基金：

中国国家自然科学基金;

关键词：

Recursive identification; data filtering; maximum likelihood; least squares; parameter estimation; MELT INDEX PREDICTION; PARAMETER-ESTIMATION ALGORITHMS; FAULT-TOLERANT CONTROL; ITERATIVE ESTIMATION; HAMMERSTEIN SYSTEMS; STATE;

D O I：

10.1177/1077546314523634

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper considers the parameter estimation problem of controlled autoregressive moving average systems. The basic idea is to use the noise polynomial to filter the input-output data, then a controlled moving average identification model and a noise model are obtained. A maximum likelihood recursive least squares algorithm and a recursive least squares algorithm are used to interactively estimate the parameters of the two identification models by using the hierarchical identification principle. A numerical example is provided to show the effectiveness of the proposed algorithms.

引用

页码：3098 / 3106

页数：9