Iterative Learning Control for Batch Weighing and Feeding Process

Due to the fact that material properties may change, weighing and feeding process is difficult to achieve high accuracy with conventional control methods. This paper discusses application of iterative learning control in weighing and feeding process, which takes advantage of previous control information. First, a continual state-space model is established based on dynamic analysis of weighing and feeding process. Next, this model is discretized into a two-dimensional state space model, which takes into account more state information about the process. Then, an iterative learning controller is presented, which improves the tracking performance with the increase of feeding times. A multi-objective simulated annealing is used to find the controller parameters with better control performance under the stability condition. Finally, simulation results are given, which show that a faster feed rate and more accurate feed accuracy is obtained after several feeding batches.