Since 1992, research of iterative learning control of a new leap.
自1992年以来，迭代学习控制研究出了新飞跃。
In this paper, martingale theory is used in iterative learning control.
本文则将鞅理论应用于迭代学习控制中。
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 discussion is made on the iterative learning control for a class of continuous systems.
对一类连续系统的迭代学习控制问题进行了讨论，提出了一种新的迭代学习控制算法。
P mode iterative learning control with variable learning factor is adopted in this paper.
通过大量试验得到迭代学习因子的变化规律，从而采用变因子p型迭代学习控制。
Suppose the controlled system is iterative learning control system with random disturbance.
设定被控系统是具有随机干扰的迭代学习控制系统。
In this paper, an optimal iterative learning control scheme for discrete systems is presented.
提出了一类离散系统的最优迭代学习控制方法。
The simulation example verifies the effectiveness of closed-loop iterative learning control law.
仿真实例说明闭环迭代学习律的有效性和快速性。
According to the model, a new dual-staged optimal iterative learning control scheme is proposed.
基于一种新的线性化近似模型，提出一类双层最优迭代算法。
In this paper a PD iterative learning control method for robots with repetitive operation is proposed.
针对具有可重复工作方式的机器人，提出了一种PD迭代学习控制方法。
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。
In this paper, an iterative learning control scheme is proposed for a class of nonlinear uncertain systems.
本文提出一种带饱和限幅的迭代学习控制器设计方法。
For repetitive movements of the objects, iterative learning control is an positive and effective control method.
对于具有重复运动性质的对象，迭代学习控制是一种有效的控制方法。
This paper presents an iterative learning control technique for batch processes based on time varying perturbation models.
为了克服模型与过程间的偏差，提出了一个基于时变偏扰模型的间歇过程迭代学习控制方法。
It can be seen that the multiple-type iterative learning control can be used in high frequency angle-vibration table control.
结论表明这种复合迭代控制器可以应用于高频角振动转台的控制。
Using identification of neural networks, a new method of robust iterative learning control algorithm is proposed in the paper.
在神经网络辨识的基础上，提出一种新的鲁棒迭代学习控制方法。
So basic iterative learning control algorithms can't be used in the case which the trajectory is square wave or triangle wave.
故基本选代学习控制是不能直接应用于类似于方波、三角波这样的曲线的跟踪中。
The research results show that using iterative learning control algorithm can improve the tracing accurate of the force system.
研究结果表明，采用迭代学习控制算法可以有效地提高力系统的跟随精度。
The design scheme of the classical D-type iterative learning control law depends on the relative degree of the controlled systems.
传统的D型迭代学习控制的控制律设计方案依赖于被控系统的相对度。
The new algorithm is different from the algorithms of iterative learning control proposed recently, and is with nonlinear structure.
这类新算法与目前所有迭代学习控制算法不同，具有非线性结构。
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。
An adaptive robust iterative learning control scheme is developed for a class of uncertain nonlinear systems, including robotics as a subset.
对一类不确定非线性系统，包括不确定性机器人，提出一种自适应鲁棒迭代学习控制方案。
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。
The actual output trajectory of the system achieved better convergence to the desired trajectory by using the iterative learning control algorithm.
利用该算法进行学习控制，使系统的实际输出能更好地收敛于系统的理想输出。
Some simulation results show the rationability of stable in the conditional expectation and the efficiency of improved iterative learning control law.
一些仿真结果表明给出的依条件期望稳定的合理性，改进的迭代学习控制律和稳定性定理的有效性。
Theoretical analysis indicates that iterative learning control algorithm is robust if initial shift and System parameter disturbance within limited bound.
理论分析表明，当系统状态初值漂移和系统参数扰动在一定范围内，迭代学习控制算法关于是鲁棒的。
Aimed at the problem of iterative learning control for nonlinear discrete time-variant system, the improved iterative learning control algorithm is given.
针对非线性离散时变系统的迭代学习控制问题，提出了一种改进的迭代学习控制算法。
Aiming at dynamic model uncertainties and load disturbances of robot manipulators, an iterative learning control scheme using neural networks is presented.
针对机器人动力学模型的不确定性和负载扰动，提出了一种采用神经网络的机器人迭代学习控制方法。
Iterative learning control is an effective technique for improving the tracking performance of systems that execute the same trajectory motion again and again.
对于完成重复轨迹跟踪任务的系统，迭代学习控制是一种能有效地改进其跟踪性能的技术。
If the initial value is not the expected output, but in its certain neighborhood, then we put such questions as the initial value of iterative learning control.
如果不在期望输出上，而是在期望输出轨迹的某一邻域上，我们把这类问题称为迭代学习控制的初值问题。
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