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 signijicant yield loss by trimming and demanded tighter width tolerances of rolled plates. This necessitates vertical rolling with considerable width accuracy. In this papec a slab width control system is proposed in order to meet the stringent requirement on the plate dimensional tolerance. The control system adopts a multi-layer perception 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 pelformance 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.