Iterative Learning Control For Periodic Disturbances in Twin-Roll Strip Casting with Measurement Delay

We consider the problem of periodic disturbances in the thickness profile of a strip produced using the twin-roll casting process. In twin-roll casting, a strip is produced by pouring molten metal on the surface of two casting rolls that simultaneously cool and compress the strip as they rotate. This rotational motion can produce periodic disturbances in the thickness profile due to angular variations in the shape and thermodynamic characteristics of the rolls. Compensating for these disturbances is further complicated by the fact that there exist large measurement delays between the initial casting and the thickness profile measurement. This paper explores the use of an iterative learning control (ILC) algorithm to reduce the influence of these disturbances on a per-revolution basis. Further consideration is given to managing the measurement delay within the ILC framework. Simulation results show that the proposed controller is able to minimize the effect of the periodic disturbance on the strip thickness profile.