Basic speed iterative learning control method and application for electro-slag furnace

Re-melting steel-making method of electro-slag furnace is widely used in the special steel processing. Descending power melting control is considered to be the effective control method which can ensure the good quality of electro-slag furnace ingots. By analyzing the process of ESR, we proposed a iterative learning constant melting rate control algorithm based on melting rate model feedforward. The basic feed speed of electrode is calculated by the model according to the re-melting theory, and proceeding iterative learning by using the batch characteristics of re-melting. So it can overcome the effect of the system´s unknown factors. Through iterative learning, the accuracy of the basic speed can be gradually improved to the optimal value, the burden of double closed loop regulation can be reduced, so that the re-melting process become more stable and the re-melting metal quality will be improved. Actual operating data show that the proposed method is effective.