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Linear Switching System Identification Applied to Blast Furnace Data

Amir H. Shirdel, Kaj-Mikael Björk, Markus Holopainen, Christer Carlsson, Hannu T. Toivonen, Linear Switching System Identification Applied to Blast Furnace Data. In: Joaquim Filipe, Kurosh Madani (Eds.), Proceedings of the 11th International Conference on Informatics in Control, 643–648, Scitepress, 2014.

http://dx.doi.org/10.5220/0005022806430648

Abstract:

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).

BibTeX entry:

@INPROCEEDINGS{inpShBjHoCaTo14a,
  title = {Linear Switching System Identification Applied to Blast Furnace Data},
  booktitle = {Proceedings of the 11th International Conference on Informatics in Control},
  author = {Shirdel, Amir H. and Björk, Kaj-Mikael and Holopainen, Markus and Carlsson, Christer and Toivonen, Hannu T.},
  editor = {Filipe, Joaquim and Madani, Kurosh},
  publisher = {Scitepress},
  pages = {643–648},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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