Innovative fuzzy-neural model predictive control synthesis for pusher reheating furnace

Goran S. Stojanovski, Mile J. Stankovski, I. Rudas, Juanwei Jing

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter is largely based on the paper “Pusher Reheating Furnace Control via Fuzzy-Neural Model Predictive Control Synthesis” presented at IEEE IS 2012 in Sofia, Bulgaria. A design of innovative 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 publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages18
Publication statusPublished - Mar 1 2016

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860949X



  • Fuzzy model predictive control
  • Fuzzy neural networks
  • Optimization
  • Set-point control
  • Time-delay processes

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Stojanovski, G. S., Stankovski, M. J., Rudas, I., & Jing, J. (2016). Innovative fuzzy-neural model predictive control synthesis for pusher reheating furnace. In Studies in Computational Intelligence (Vol. 586, pp. 251-268). (Studies in Computational Intelligence; Vol. 586). Springer Verlag.