Analysis of Grey Prediction Based Iterative Learning Control

This paper presents a novel iterative learning control method based on the research of grey theory. The grey predictor is applied to extract key information and reduce the randomness of the measured non-stationary time series signals from sensors, and send the prediction information to the iterative learning controller. This design can not only reduce the trajectory tracking error of reference input but also improve the learning rate. The complete mathematical model is derived and the sufficient condition for convergence is given. At last, experimental results obtained from two plants show that the tracking accuracy is much improved when the proposed new method is applied.