Iterative learning control (ILC) is suitable for systems that are able to repeatedly complete
several tasks over a fixed time interval. Since it was first proposed, ILC has been further …

This paper proposes a unified design framework for data-driven optimality-based
generalized iterative learning control (DDOGILC), including data-driven optimal ILC …

This paper presents a data-driven optimal terminal iterative learning control (TILC) approach
for linear and nonlinear discrete-time systems. The iterative learning control law is updated …

In this paper, an improved data-driven point-to-point iterative learning control is proposed for
nonlinear repetitive systems where only the system outputs at the multiple intermediate …

In this paper, we present two iterative learning control (ILC) frameworks for tracking
problems with specified data points that are desired points at certain time instants. To design …

This brief considers the iterative learning control (ILC) problem when tracking is only
required at a subset of isolated time points along the trial duration. It presents a norm …

In this study, a novel data‐driven terminal iterative learning control with high‐order learning
law is proposed for a class of non‐linear non‐affine discrete‐time multiple‐input–multiple …

In this paper, an enhanced data-driven optimal terminal iterative learning control (E-
DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A …

摘要本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.
基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型, 其研究较为成熟和完善 …

The paper describes a substantial extension of norm optimal iterative learning control
(NOILC) that permits tracking of a class of finite dimensional reference signals whilst …