A high performance tracker for the sampled-data system based on iterative learning control

A high performance tracker for the unknown stochastic sampled-data system based on iterative learning control (ILC) to be tracked is proposed in this paper. The newly developed ILC algorithm and the high-gain property tracker are first combined to speed up the convergence rate. The Euler method based difference type (D-type) ILC algorithm is proposed to combine with the prediction-based digital redesign method to construct the high performance modified D-type ILC for the model-based sampled-data systems. Finally, some examples are given for illustrating the effectiveness of the newly proposed method.