Convergence analysis of quantized iterative learning control using lifting representation

This paper considers the convergence of iterative learning control (ILC) for discrete time systems with data quantization. Two iterative learning control schemes are proposed by using the system output quantized signal and tracking error quantized signal. Based on the super-vector formulation of the ILC systems, the convergence conditions for the two iterative learning control schemes are given respectively. It is shown that the ILC law with system output quantized signal only make the tracking error converge to a bound, thus the ILC law with tracking error quantized signal can obtain zero tracking error. The results are illustrated by a numerical example.