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

Goran Stojanovski, Mile Stankovski, I. 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 - 2012
Event2012 6th IEEE International Conference Intelligent Systems, IS 2012 - Sofia, Bulgaria
Duration: Sep 6 2012Sep 8 2012

Other

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

Fingerprint

Model predictive control
Furnaces
Industrial furnaces
Controllers
Dynamic models

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Stojanovski, G., Stankovski, M., Rudas, I., & 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] https://doi.org/10.1109/IS.2012.6335229

Pusher reheating furnace control via fuzzy-neural model predictive control synthesis. / Stojanovski, Goran; Stankovski, Mile; Rudas, I.; Jing, Juanwei.

IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. 2012. p. 272-278 6335229.

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

Stojanovski, G, Stankovski, M, Rudas, I & 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., 6335229, pp. 272-278, 2012 6th IEEE International Conference Intelligent Systems, IS 2012, Sofia, Bulgaria, 9/6/12. https://doi.org/10.1109/IS.2012.6335229
Stojanovski G, Stankovski M, Rudas I, Jing J. Pusher reheating furnace control via fuzzy-neural model predictive control synthesis. In IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. 2012. p. 272-278. 6335229 https://doi.org/10.1109/IS.2012.6335229
Stojanovski, Goran ; Stankovski, Mile ; Rudas, I. ; Jing, Juanwei. / Pusher reheating furnace control via fuzzy-neural model predictive control synthesis. IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. 2012. pp. 272-278
@inproceedings{afeb60c11b784c409a051d5038f22afe,
title = "Pusher reheating furnace control via fuzzy-neural model predictive control synthesis",
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.",
keywords = "fuzzy model predictive control, Fuzzy-neural models, optimization, set-point control, time-delay processes",
author = "Goran Stojanovski and Mile Stankovski and I. Rudas and Juanwei Jing",
year = "2012",
doi = "10.1109/IS.2012.6335229",
language = "English",
isbn = "9781467327824",
pages = "272--278",
booktitle = "IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings",

}

TY - GEN

T1 - Pusher reheating furnace control via fuzzy-neural model predictive control synthesis

AU - Stojanovski, Goran

AU - Stankovski, Mile

AU - Rudas, I.

AU - Jing, Juanwei

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - fuzzy model predictive control

KW - Fuzzy-neural models

KW - optimization

KW - set-point control

KW - time-delay processes

UR - http://www.scopus.com/inward/record.url?scp=84869803362&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84869803362&partnerID=8YFLogxK

U2 - 10.1109/IS.2012.6335229

DO - 10.1109/IS.2012.6335229

M3 - Conference contribution

SN - 9781467327824

SP - 272

EP - 278

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

ER -