Innovative Fuzzy-Neural Model Predictive Control Synthesis for Pusher Reheating Furnace

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.