Iterative learning control for stretch reducing process of seamless pipe

Based on the iterative learning control (ILC) theory, the online learning algorithms for stretch reducing process of seamless pipe were proposed for average wall thickness control (WTCA) and crop end control (CEC), in which the measured wall thickness and specifications of the tubes are used to compensate for the disturbance caused by the system uncertainty and the mathematical model error through adjusting rollers steady speed and dynamic speed iteratively. The results of practical experiments in Baosteel company demonstrated the effectiveness of this new method.