Iterative learning control (ILC) is a high performance control scenario that is widely applied
to systems that repeat a given task or operation defined over a finite duration, and has been …

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

Iterative learning control (ILC) aims to maximize the performance of systems performing
repeated tracking tasks. However, in most existing applications, the motion profile is …

Abstract Iterative Learning Control (ILC) can significantly enhance the performance of
systems that perform repeating tasks. However, small variations in the performed task may …

Trial-varying disturbances are a key concern in Iterative Learning Control (ILC) and may
lead to inefficient and expensive implementations and severe performance deterioration …

Learning from past data enables substantial performance improvement for systems that
perform repeating tasks. Achieving high accuracy and fast convergence in the presence of …

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) algorithms enable high‐performance control design using
only approximate models of the system. To deal with severe modeling errors, a robustness …

Iterative learning control (ILC) enables high performance for systems that execute repeating
tasks. Norm-optimal ILC based on lifted system representations provides an analytic …

Use of a susceptor (a metallized film attached to paperboard) in microwave heating can
emulate conventional heating with benefits such as cris** food. This study develops a …