A neural network approach to the control of the plate width in hot plate mills

Deviation of a slab width from the desired value in hot plate mills has caused significant yield loss by trimming and led to a demand for tighter width tolerances of rolled plates. This necessitates vertical rolling with considerable width accuracy. In this paper, a slab width control system is proposed in order to meet the stringent requirement on the plate dimensional tolerance. The control system adopts a multilayer perceptron neural network to account for the complicated process dynamics characterized by nonlinear, time-varying and uncertain properties. A series of simulation works were conducted to evaluate the performance of the proposed control system for various operating conditions and networks design parameters. The control performance is analyzed in detail in terms of the system response accuracy and robustness to rolling temperature variation