Maximum Likelihood Recursive Generalized Extended Least Squares Estimation Methods for a Bilinear-parameter Systems with ARMA Noise Based on the Over-parameterization Model

Maximum likelihood methods have wide applications in system modeling and parameter estimation. For the purpose of improving the precision of parameter estimation, this paper presents a maximum likelihood recursive generalized extended least squares (ML-RLS) algorithm for a bilinear-parameter system...

Full description

Saved in:
Bibliographic Details
Published in:International journal of control, automation, and systems Vol. 20; no. 8; pp. 2606 - 2615
Main Authors: Liu, Haibo, Wang, Junwei, Ji, Yan
Format: Journal Article
Language:English
Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.08.2022
Springer Nature B.V
제어·로봇·시스템학회
Subjects:
Algorithms
Autoregressive moving average
Control
Control Theory and Applications
Engineering
Least squares
Mathematical models
Mechatronics
Parameter estimation
Parameter identification
Parameterization
Robotics
제어계측공학
Bilinear-parameter system
parameter estimation
recursive identification
least squares
maximum likelihood
ISSN:1598-6446, 2005-4092
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Maximum likelihood methods have wide applications in system modeling and parameter estimation. For the purpose of improving the precision of parameter estimation, this paper presents a maximum likelihood recursive generalized extended least squares (ML-RLS) algorithm for a bilinear-parameter system with autoregressive moving average noise based on the over-parameterization identification model. An over-parameterization-based recursive generalized extended least squares algorithm is presented to show the effectiveness of the proposed ML-RLS algorithm for comparison. The simulation test shows that the proposed algorithm has a higher estimation accuracy than the recursive least squares algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
http://link.springer.com/article/10.1007/s12555-021-0367-7
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0367-7