Distributed model predictive control of steel slab reheating furnace

Nowadays, distributed model predictive control (DMPC) is known as an attractive strategy to control a class of large-scale systems. In this paper, a DMPC algorithm based on Nash optimality is developed for online optimization and control of a class of large-scale systems with output coupling. This DMPC algorithm is further applied to the control of a reheating furnace in steel industry, illustrating the effectiveness of the proposed approach in terms of energy consumption and tracking performance in comparison to control strategies currently implemented in the steel group ArcelorMittal.