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 …

Based on a nonlifted iterative dynamic linearization formulation, a novel data-driven higher
order optimal iterative learning control (DDHOILC) is proposed for a class of nonlinear …

Iterative learning control (ILC) has been successfully applied to numerous batch processes
over the past decades. Monotonic convergence of tracking error is a desired characteristic …

This paper proposes two novel improved iterative learning control (ILC) schemes for
systems with randomly varying trial lengths. Different from the existing works on ILC with …

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

This article considers the zero-error tracking problem of quantized iterative learning control
for a general networked structure where the data are quantized and transmitted through …

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 …

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 …

For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an
iterative learning robust predictive fault-tolerant control approach is developed. Based on …

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 …