The robust iterative learning control (RILC) can deal with the systems with unknown time-
varying uncertainty to track a repeated reference signal. However, the existing robust …

This paper provides an inverse-model-based iterative learning control (ILC) for the unknown
multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel …

Iterative learning control (ILC) enables high control performance through learning from the
measured data, using only limited model knowledge in the form of a nominal parametric …

Learning to perform perfect tracking tasks based on measurement data is desirable in the
controller design of systems operating repetitively. This motivates this article to seek an …

The iterative learning control (ILC) problem is addressed in this paper for stochastic linear
systems with random data dropout modeled by a Bernoulli random variable. Both …

The piezo-driven nanopositioning stage (PNS) is a key device to provide fast and precise
motions for applications such as micromanipulation, microfabrication, and microscopy …

Iterative learning control (ILC) enables high performance for motion systems executing
repetitive tasks. The robustness filter of ILC enhances the robustness wrt model …

Optimal iterative learning control (OILC) has been recognized as an excellent model-based
means for regulating batch process with abundant successful applications reported in the …

In this work, sampled‐data iterative learning control (ILC) method is extended to a class of
continuous‐time nonlinear systems with iteration‐varying trial lengths. In order to propose a …

For iterative learning control (ILC) algorithms to date, there is a fundamental tradeoff
between plant model knowledge and convergence rate in the iteration domain. This article …