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

Learning control enables significant performance improvement for systems that perform
repeating tasks. Achieving high tracking performance by utilizing past error data typically …

The learning from data and information has led to impressive achievements in recent years.
Computer algorithms are now capable to successfully learn in many domains, including …

The productivity and product quality of many manufacturing systems hinge on the
performance of mechatronic positioning systems. For example in industrial large-format …

In this paper, a hybrid-based model-free iterative learning control algorithm is proposed to
improve the robustness and convergence speed of model-free iterative learning control in …

In order to meet the high demands in testing, actuators must be able to follow their desired
displacement with high precision. Feedforward control enables high tracking performance of …

The bulk of well‐known control techniques for time‐delay systems are sensitive to the
system's uncertainty. For unknown nonrepetitive iteration‐independent and iteration‐varying …

摘要由數據驅動的迭代學習控制可以通過消除參數系統表示中的擬合誤差,
並實現了比基於模型的迭代學習控制更優秀的軌跡追蹤性能. 在頻域中, 目前現有的數據驅動 …

Abstract Iterative Learning Control (ILC) enables high control performance through learning
from measured data, using limited model knowledge, typically in the form of a nominal …

Learning control methods enable significant performance improvements for systems that
operate repetitively. Typical methods rely on a parametric plant model to achieve fast and …