Linear switching system identification applied to blast furnace data

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Amir H. Shirdel , Kaj-Mikael Björk , Markus Holopainen , Christer Carlsson, Hannu T. Toivonen
Editors: Joaquim Filipe, Oleg Yu. Gusikhin, Kurosh Madani, Jurek Z. Sasiadek
Publication year: 2014
Publisher: IEEE Computer Society Institute of Electrical and Electronic Engineers
Book title: 11th International Conference on Informatics in Control, Automation and Robotics
Volume number: 1
Start page: 643
End page: 648
ISBN: 978-989-758-039-0


Switching systems are dynamical systems which can switch between a number of modes characterized by different dynamical behaviors. Several approaches have recently been presented for experimental identification of switching system, whereas studies on real-world applications have been scarce. This paper is focused on applying switching system identification to a blast furnace process. Specifically, the possibility of replacing nonlinear complex system models with a number of simple linear models is investigated. Identification of switching systems consists of identifying both the individual dynamical behavior of model which describes the system in the various modes, as well as the time instants when the mode changes have occurred. In this contribution a switching system identification method based on sparse optimization is used to construct linear switching dynamic models to describe the nonlinear system. The results obtained for blast furnace data are compared with a nonlinear model using Artificial Neural Fuzzy Inference System (ANFIS).


Blast Furnace, Linear Switching System, Sparse Optimization, System Identification

Last updated on 2020-25-02 at 04:12