Iterative learning control with data-driven-based compensation
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 …
varying uncertainty to track a repeated reference signal. However, the existing robust …
[HTML][HTML] Inverse-model-based iterative learning control for unknown MIMO nonlinear system with neural network
Y Lv, X Ren, J Tian, X Zhao - Neurocomputing, 2023 - Elsevier
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 …
multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel …
Multivariable iterative learning control design procedures: From decentralized to centralized, illustrated on an industrial printer
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 …
measured data, using only limited model knowledge in the form of a nominal parametric …
Design and analysis of data-driven learning control: an optimization-based approach
D Meng, J Zhang - IEEE Transactions on Neural Networks and …, 2021 - ieeexplore.ieee.org
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 …
controller design of systems operating repetitively. This motivates this article to seek an …
Two updating schemes of iterative learning control for networked control systems with random data dropouts
D Shen, C Zhang, Y Xu - Information Sciences, 2017 - Elsevier
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 …
systems with random data dropout modeled by a Bernoulli random variable. Both …
Neural network-based iterative learning control of a piezo-driven nanopositioning stage
The piezo-driven nanopositioning stage (PNS) is a key device to provide fast and precise
motions for applications such as micromanipulation, microfabrication, and microscopy …
motions for applications such as micromanipulation, microfabrication, and microscopy …
Dual-loop iterative learning control with application to an ultraprecision wafer stage
M Li, T Chen, R Cheng, K Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Iterative learning control (ILC) enables high performance for motion systems executing
repetitive tasks. The robustness filter of ILC enhances the robustness wrt model …
repetitive tasks. The robustness filter of ILC enhances the robustness wrt model …
Optimal iterative learning control for batch processes in the presence of time-varying dynamics
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 …
means for regulating batch process with abundant successful applications reported in the …
Sampled‐data iterative learning control for continuous‐time nonlinear systems with iteration‐varying lengths
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 …
continuous‐time nonlinear systems with iteration‐varying trial lengths. In order to propose a …
An improved approach to iterative learning control for uncertain systems
AA Armstrong, AJW Johnson… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …
between plant model knowledge and convergence rate in the iteration domain. This article …