Pusher reheating furnace control via fuzzy-neural model predictive control synthesis

Goran Stojanovski, Mile Stankovski, Imre J. Rudas, Juanwei Jing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A design of fuzzy model-based predictive control for industrial furnaces has been derived and applied to the model of three-zone 25 MW RZS pusher furnace at Skopje Steelworks. The fuzzy-neural variant of Sugeno fuzzy model, as an adaptive neuro-fuzzy implementation, is employed as a predictor in a predictive controller. In order to build the predictive controller the adaptation of the fuzzy model using dynamic process information is carried out. Optimization procedure employing a simplified gradient technique is used to calculate predictions of the future control actions.

Original languageEnglish
Title of host publicationIS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings
Pages272-278
Number of pages7
DOIs
Publication statusPublished - Nov 28 2012
Event2012 6th IEEE International Conference Intelligent Systems, IS 2012 - Sofia, Bulgaria
Duration: Sep 6 2012Sep 8 2012

Publication series

NameIS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings

Other

Other2012 6th IEEE International Conference Intelligent Systems, IS 2012
CountryBulgaria
CitySofia
Period9/6/129/8/12

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Keywords

  • Fuzzy-neural models
  • fuzzy model predictive control
  • optimization
  • set-point control
  • time-delay processes

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Stojanovski, G., Stankovski, M., Rudas, I. J., & Jing, J. (2012). Pusher reheating furnace control via fuzzy-neural model predictive control synthesis. In IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings (pp. 272-278). [6335229] (IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings). https://doi.org/10.1109/IS.2012.6335229