Studied the convergence of ILC based on the 2-d theory.

基于2 - D线性系统理论研究了迭代学习控制的收敛性问题。

D-type iterative learning control (ILC) law is one of the main laws of ILC.

型迭代学习控制律是迭代学习控制的一种主要学习律。

A machine with the power of the ILC might provide clues about how to do this.

一部有着国际线性冲力机力量的设备也许能够为人们提供一些成就与此的指引。

This article mainly study the initial value problem of time-delayed PD-type ILC.

本文主要讨论了带时滞超前PD型迭代学习控制的初值问题。

The classical D-type ILC law depends on the relative degree of the controlled system.

传统的D型迭代学习控制律依赖于被控系统的相对度。

Conclusion Breast sonography has limitation to diagnose ILC, particularly the smaller ILC.

结论超声对诊断ilc具有局限性，特别是对小的ILC。

ILC had also been applied in the study of interactions between proteins and biological membrane.

用固定化脂质体色谱固定相还可以进行蛋白与生物膜相互作用的研究。

Robust gradient-type iterative learning control (ILC) was studied for a class of uncertain linear systems.

针对不确定的线性系统，研究鲁棒梯度型迭代学习控制的设计问题。

The issues of iterative learning controller design and disturbance rejection in ILC are addressed in this paper.

针对不确定线性定常系统，考虑频域迭代学习控制器设计及干扰抑制问题。

It is shown that the multiple time delay in state variables do not affect the ILC convergence property significantly.

研究结果表明，系统状态的时滞对非线性系统的迭代学习控制没有明显的影响。

Designs of the iterative learning algorithms, the most important problems in the ILC, are also studied in this dissertation.

迭代学习算法设计一直是迭代学习控制研究的重点，本文从一些新的视角做了探讨。

The paper also gives a case of the electro-hydraulic position servo control system to prove the validity of the proposed ILC.

本文还给出了提出的迭代学习控制律在一个电液位置伺服控制系统中的应用实例，来验证算法的有效性。

Considering the real execution of a two-link robotic manipulator arm, D-type ILC can't achieve the effects of simulation results.

考虑到两连杆机械臂的实际执行过程，D型迭代学习控制并不能达到仿真结果中的效果。

The theory of iterative learning control was used to ensure the astringency and stability of ILC in single-in-single-out non-linear system.

基于迭代学习控制的基本原理，阐述了单输人单输出非线性系统中il的收敛性和稳定性的一般性结论。

The simulation result shows that the proposed ILC can track the reference signal according to the reference signal and the input-output data;

实验结果表明：本文提出的迭代学习律，只需要根据参考信号及输入输出信号就能实现对参考信号的完全跟踪；

A new iterative learning control (ILC) updating law is proposed for the tracking control of continuous linear system over a finite time interval.

提出了一个新的迭代学习控制(ilc)更新律用于连续线性系统的有限时间区间跟踪控制。

Then introduce a new theory of ILC based on prescribed input-output subspace, it requires neither derivatives of the error signals nor dual mapping.

然后介绍了一种新的基于指定输入—输出空间的迭代学习控制思想，它即不需要对误差信号求导数，也不需要知道被控对象的对偶映射。

This method not only improves disturbance and robustness of the controlled system, but also fully exerts the intellectualized virtue of ILC without precise model.

此方法不仅提高了系统的抗干扰性能和初始鲁棒性，而且充分发挥了迭代学习控制不需要建立精确数学模型的智能化优点。

In this paper, the effect of annealing temperature and time on the rate of metal-induced lateral crystallization (m ILC) of amorphous silicon are investigated in detail.

详细研究了金属诱导非晶硅横向晶化时间、温度对晶化生长的影响。

In this dissertation, firstly basic knowledge about iterative learning control is introduced, including its history, mathematic description and some common ILC schemes etc.

本文首先对迭代学习控制的一些基本知识进行了简单介绍，包括提出的历史，数学描述以及一些常用迭代学习控制律。

The idea of ILC is to gradually revise imperfect control input using the error between system output and the desired trajectory and realize perfect tracking in a finite time interval.

迭代学习控制的基本思想是基于输出信号与给定目标轨迹的偏差不断修正不理想的控制输入信号，实现在有限时间区间的完全跟踪任务。

The new learning control rule not only incorporates a state compensation in the conventional ILC formula, but also adopts the wavelet transform to filter learnable tracking errors without phase shift.

在新控制算法中，除了在传统算法基础上增加了状态补偿外，还引用了小波变换来对跟踪误差进行了滤波而没有相位补偿。

The new learning control rule not only incorporated a state compensation in the conventional ILC formula but also adopted the wavelet transform to filter learnable tracking errors without phase shift.

在新控制算法中，除了在传统算法基础上增加了状态补偿外，还引用了小波变换来对跟踪误差进行了滤波而没有相位补偿。

Compared with the above two ILCs, the convergence condition of P-type ILC is much stricter and its robustness is worse. Besides, convergence speed of P-type ILC is much slower than that of D-type ILC.

与这两种迭代学习控制相比，P型迭代学习控制的收敛条件更加严苛，收敛性和鲁棒性较差，而且收敛速度比以上两种控制方法要慢得多。