A capital issue in roll-gap control for rolling mill plants is the difficulty to measure the output thickness without including time delays in the control loop. Time delays are a consequence of the possible locations for the output thickness sensor, which usually is located some distance away from the roll gap. In this work, a new model-based predictive control law is proposed. The new scheme is a neural network based predictive control structure which is applied to roll-gap control with outstanding results. It is shown that the neural network based predictive control permits to overcome the existing time delays in the system dynamics. The proposed scheme implements a virtual thickness sensor, which releases an accurate estimate of the actual output thickness. It is shown that the dynamic response of the rolling mill system can be substantially improved by using the proposed controller. Simulation results are presented to illustrate the controller performance.
Model-based predictive control; neural networks; agc systems; steel industry
