Linear switching system identification applied to blast furnace data

Amir Shirdel, Kaj-Mikael Björk, Markus Holopainen, Christer Carlsson, Hannu Toivonen

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    1 Citeringar (Scopus)

    Sammanfattning

    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). 
    OriginalspråkOdefinierat/okänt
    Titel på gästpublikation11th International Conference on Informatics in Control, Automation and Robotics
    RedaktörerJoaquim Filipe, Oleg Yu. Gusikhin, Kurosh Madani, Jurek Z. Sasiadek
    FörlagIEEE Computer Society Institute of Electrical and Electronic Engineers
    Sidor643–648
    ISBN (tryckt)978-989-758-039-0
    DOI
    StatusPublicerad - 2014
    MoE-publikationstypA4 Artikel i en konferenspublikation
    Evenemangconference; 2014-09-01; 2014-09-03 - Vienna, Austria
    Varaktighet: 1 sep 20143 sep 2014

    Konferens

    Konferensconference; 2014-09-01; 2014-09-03
    Period01/09/1403/09/14

    Nyckelord

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

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