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。
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。
This paper combines learning theory with robust control and discusses robust control design problems involving real parameter uncertainty in control systems based on randomized algorithms.
将学习理论与鲁棒控制相结合，采用随机化算法针对实参数不确定系统讨论了鲁棒控制器的设计问题。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
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。
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 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 iterative learning control is studied for the procedure of industrial process steady state optimization in order to improve the dynamic performance of control systems.
针对具有噪声的工业过程稳态优化进程，提出迭代学习控制以期改善控制系统的动态品质。
A fighter safe landing lateral-directional control method is presented based on reinforcement learning algorithm (RL), using hierarchical control of dynamical large-scale systems theory.
基于大系统递阶控制思想，提出了一种运用再励学习算法设计歼击机自动着陆横侧向协调控制系统的方法。
An adaptive iterative learning control approach is proposed for a class of single input, single output uncertain nonlinear systems with completely unknown high frequency learning gain.
针对一类单输入单输出不确定非线性重复跟踪系统，提出一种基于完全未知高频反馈增益的自适应迭代学习控制。
By contrast, loosely-coupled systems affirmatively eschew this level of control, and build in room for human agency, experimentation, failure, communication, learning and adaptation.
相反，松散结合的系统遗弃了这种等级的控制，而是为人类自主性、试验、失败、交流、学习和适应提供空间。
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。
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。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
The deterministic learning theory will provide a new approach to data-based modeling, recognition, control of complex processes and systems.
本文表明确定学习可以为时态数据挖掘的研究提供新的途径，并为基于数据的建模与控制等问题提供新的研究思路。
Feedback is provided not from of a teaching process as in supervised learning, but as punishments and rewards in the environment. Example problems are systems and robot control.
反馈并不像监督学习那样来自于训练的过程，而是作为环境的惩罚或者是奖赏。
Feedback is provided not from of a teaching process as in supervised learning, but as punishments and rewards in the environment. Example problems are systems and robot control.
反馈并不像监督学习那样来自于训练的过程，而是作为环境的惩罚或者是奖赏。
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