Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems

This paper concerns the parameter identification methods of multivariable equation-error systems. By means of the decomposition technique, the multivariable identification model is transformed into two sub-identification models and a decomposition-based stochastic gradient (D-SG) algorithm is presen...

Full description

Saved in:
Bibliographic Details
Published in:International journal of control, automation, and systems 2019, Automation, and Systems, 17(8), , pp.2037-2045
Main Authors: Lu, Xian, Ding, Feng, Alsaedi, Ahmed, Hayat, Tasawar
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Control
Decomposition
Engineering
Identification methods
Innovations
Mathematical models
Matrix algebra
Matrix methods
Mechatronics
Parameter estimation
Parameter identification
Regular Papers
Robotics
제어계측공학
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 paper concerns the parameter identification methods of multivariable equation-error systems. By means of the decomposition technique, the multivariable identification model is transformed into two sub-identification models and a decomposition-based stochastic gradient (D-SG) algorithm is presented for estimating the parameters of these two submodels. In order to further improve the convergence rate and the parameter estimation accuracy, we expand the innovation vectors to the innovation matrices and develop a decomposition-based multi-innovation stochastic gradient (D-MISG) algorithm. The simulation results confirm that the D-MISG algorithm can provide more accurate parameter estimates than the D-SG algorithm.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-018-0875-2