Multiple-page mapping artificial neural network algorithm used for constant tension control

Constant tension control is widely required in industrial applications such as paper machines, coating machines, rewinding and unwinding machines. In a metal film coating machine, which is a multi-input multi-output system, speed and tension have cross coupling and thus desired speed and tension responses are difficult to achieve by applying conventional analogue proportional-plus-integral (PI) control. This paper introduces a multiple-page mapping artificial neural network with back-propagation training algorithm. This method can successfully decouple the speed and tension control loops and both loops can operate quasi-independently. It overcomes the disadvantages of traditional PI control systems. To handle the variation of the rewinding roll diameter, multiple pages of neural networks are applied. Some simulation results show the effectiveness of this control algorithm.