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 …

In practical applications, due to the limited communication bandwidth, the network control
systems (NCSs) are prone to data dropouts when the load is high. In this paper, the problem …

Learning control is investigated to solve the tracking problem for linear systems via
unreliable networks with random data dropouts. By using an encoding-decoding mechanism …

In this study, the zero‐error convergence of the iterative learning control for a tracking
problem is realised by incorporating a uniform quantiser with an encoding and decoding …

In this study, the problem of iterative learning control (ILC) for discrete‐time systems with
quantised output measurements is considered. Here, a logarithmic quantiser is introduced …

This work explores the problem of uniform quantization of iterative learning control (ILC) for
nonlinear nonaffine systems under a data‐driven design and analysis framework. First, to …

In this article, we consider quantized learning control for linear networked systems with
additive channel noise. Our objective is to achieve high tracking performance while reducing …

This paper considers the problem of data driven iterative learning control (DDILC) for a class
of nonaffine nonlinear systems subject to data quantization and sensor saturation. Two …

This paper presents a stability analysis of the iterative learning control for discrete‐time
systems with data quantization. Three quantized iterative learning control schemes are …

An iterative learning control (ILC) algorithm using quantized error information is given in this
paper for both linear and nonlinear discrete-time systems with stochastic noises. A …