Application of iterative learning control to coil-to-coil control in rolling

Iterative learning control is a feedforward control technique applied to systems or processes that operate in a repetitive fashion over a fixed interval of time to improve tracking/regulation performance in response to reference inputs/disturbance inputs that are repeatable in each cycle. In this paper, learning control is applied to coil-to-coil gauge and tension control during the thread-up phase of a single stand cold mill, to compensate for disturbances caused by the variation of roll bite friction. Simulations are carried out to demonstrate the effectiveness of learning control.