Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model

This communique uses the auxiliary model method to study the identification problem of a multiple-input multiple-output (MIMO) system. For such a MIMO system whose outputs are contaminated by an ARMA noise process (i.e., correlated noise), an auxiliary model based recursive least squares parameter e...

Full description

Saved in:
Bibliographic Details
Published in:Automatica (Oxford) 2016-09, Vol.71, p.308-313
Main Authors: Wang, Yanjiao, Ding, Feng
Format: Article
Language:English
Subjects:
Auxiliary model
Hierarchical identification
Least squares
MIMO system
Recursive identification
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This communique uses the auxiliary model method to study the identification problem of a multiple-input multiple-output (MIMO) system. For such a MIMO system whose outputs are contaminated by an ARMA noise process (i.e., correlated noise), an auxiliary model based recursive least squares parameter estimation algorithm is presented through filtering input–output data. The proposed algorithm has higher estimation accuracy than the existing multivariable identification algorithm. The simulation example is given.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2016.05.024