Effect analysis of data dropout on iterative learning control

In this paper, the effect analysis of data dropout on iterative learning control (ILC) for linear discrete-time systems is considered. Using the lifting technique to ILC, the condition of tracking error for both asymptotic stability and monotonic convergence is first given, and then the relationship between convergence speed and data dropout rate is also presented. It is shown that, although data dropout not change the convergence of the ILC algorithm, the convergent speed gets slower as dropout rate increases. Two examples are given to validate the theoretical results.