ABSTRACT Iterative learning control (ILC) applies to systems where the same finite‐duration task is completed repeatedly or where there is a stoppage time between finite‐duration tasks. Each completion is commonly termed a trial, and the control law can use all data generated on previous trials. Given a supplied reference trajectory, the error on any trial is the difference between this trajectory and the trial output. The design problem is to find a series of control signals, one for each trial, that will reduce the error from trial to trial. The trial‐to‐trial error convergence rate is a critical part of an ILC law design. A desirable feature is obtaining the fastest possible convergence rate consistent with an acceptable dynamic response along each trial. This paper considers an ILC design problem for discrete systems with changing the reference trajectory and parameters under input saturation. A new approach is proposed to accelerate convergence, based on a combination of the gradient optimization method and the authors' method of vector Lyapunov functions for repetitive processes. The ILC law switches depending on the reference trajectory change for reducing the transient error caused by this change. A supporting example is given, demonstrating the effectiveness of the design proposed.
Pakshin et al. (Thu,) studied this question.