Robust discrete-time iterative learning control: initial shift problem

This paper is concerned with the initial shift problem of iterative learning control for a class of nonlinear discrete-time systems with well-defined relative degree. The information from several previous operation cycles is used and the learning algorithm is shown to be robust with respect to initial shifts. In the presence of an initial shift, the converged output trajectory is assessed as the iteration number increases. Initial rectifying action is an alternative approach to address the initial shift problem and is proved to ensure complete tracking with a transitional trajectory