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

Author

Stojanovski, G. ; Stankovski, Mile ; Rudas, Imre J. ; Juanwei Jing

Author_Institution

Fac. of Electr. Eng. & Inf. Technol., SS Cyril & Methodius Univ., Skopje, Macedonia

fYear

2012

fDate

6-8 Sept. 2012

Firstpage

272

Lastpage

278

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

adaptive control; furnaces; fuzzy neural nets; fuzzy set theory; gradient methods; industrial control; neurocontrollers; optimisation; predictive control; Skopje Steelworks; Sugeno fuzzy model; adaptive neuro-fuzzy implementation; control actions; dynamic process information; fuzzy-neural model predictive control synthesis; fuzzy-neural variant; industrial furnaces; optimization procedure; power 25 MW; pusher reheating furnace control; simplified gradient technique; three-zone RZS pusher furnace; Adaptation models; Furnaces; Mathematical model; Optimization; Predictive control; Predictive models; Fuzzy-neural models; fuzzy model predictive control; optimization; set-point control; time-delay processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335229

Filename

6335229