Iterative learning control (ILC) is a high‐performance technique for repeated control tasks
with design postulates on a fixed reference profile and identical initial conditions. However …

Manufacturing equipment and scientific instruments, including wafer scanners, printers,
microscopes, and medical imaging scanners, require accurate and fast motions. An increase …

This work reports on a novel approach to effective design of iterative learning control of
repetitive nonlinear processes based on artificial neural networks. The essential idea …

The repetitive tracking task for time‐varying systems (TVSs) with non‐repetitive time‐varying
parameters at each trial, which is also called non‐repetitive TVSs, is realized in this article …

In the realm of ultraprecision motion control, achieving high tracking accuracy, great
trajectory generalization, and robust disturbance rejection simultaneously remains a …

Abstract Models of inverse systems are commonly encountered in control, eg, feedforward.
The aim of this paper is to address several aspects in identification of inverse models …

The classical problem setup of iterative learning control (ILC) is to enforce tracking of a
reference profile specified at all time points in the fixed task duration. The removal of the time …

The standard assumption that a measurement signal is available at each sample in iterative
learning control (ILC) is not always justified, eg, when exploiting time-stamped data from …

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 …

Machine learning techniques, including Gaussian processes (GPs), are expected to play a
significant role in meeting speed, accuracy, and functionality requirements in future data …