Abstract
This paper describes an application of adaptive predictive control for the bottom temperature of a glass furnace. This control is part of a hierarchical control structure which aims at optimizing the working of the furnace, from an economical point of view. The control of the bottom temperature is a difficult part in this control structure, because of the poor physical knowledge of the system and the very large time constants. The control design is based on a simple low order linear model with unknown and possibly time varying parameters. The time variations of these parameters can account for the variations of the simple linear model with the load. The control algorithm is based on the classical Clarke-Gawthrop self tuning algorithm. However, in this particular case, the classical structure has been modified in order to introduce specifically a feedforward compensation of the load. This algorithm has been tested through several simulations. It has then been implemented on line on the real process and has been in operation for more than a year, showing the real possibilities of these modern control techniques on industrial processes.
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