The paper discusses a new self-learning control system.
本文讨论一种新型的自学习控制系统。
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。
Research on Learning Control with distal teacher of chaotic dynamical system.
连续混沌动力学系统的远程学习控制研究。
To this end, the fuzzy model reference learning control (FMRLC) is presented.
为此，设计了防抱制动系统的模糊模型参考学习控制器。
This paper proposes a learning control based high-speed scanning mode for an AFM system.
本文针对原子力显微镜系统，提出了一种基于学习控制的快速扫描模式。
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。
For Variable Valve Timing (VVT) system, a kind of self-learning control method was proposed.
针对可变相位机构，提出了一种自学习型的控制方法。
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。
A self learning control method is applied to optimize performance of internal combustion engine.
提出了一种可在内燃机电子控制中用于优化内燃机性能的自学习控制方法。
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。
A new fuzzy adaptive learning control (FALCON), structure based mixture learning control is put forward in the paper.
文中提出一种基于模糊自适应学习控制(FALCON)结构下新型的混合学习控制策略。
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。
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。
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。
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。
Not only Intelligent control and self-learning control but also the basic principle of the iteration self-learning control are described.
概述了智能控制和自学习控制，阐述了迭代自学习控制的基本原理。
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。
A novel repetitive learning control method is proposed for the tracking or rejection of unknown periodic reference or disturbance with a known period.
为跟踪或抑制仅周期已知的未知周期参考或扰动信号，提出一种新的重复学习控制方法。
To make a robot track a given desired motion trajectory, a new learning control scheme is proposed which is based on the repeatability of robot motion.
为了使机器人跟踪给定的期望轨线，提出了一种新的基于机器人运动重复性的学习控制法。
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。
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。
For reinforcement learning control in continuous Spaces, a Q-learning method based on a self-organizing fuzzy RBF (radial basis function) network is proposed.
针对连续空间下的强化学习控制问题，提出了一种基于自组织模糊rbf网络的Q学习方法。
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。
应用推荐