学习律 meaning in English
law of learning
Examples
- A series of robust ilc laws are proposed and the effectiveness of every law is guaranteed by the theory analysis and illustrated by the simulation studies
每种迭代学习律的有效性都得到了理论分析和仿真验证的双重保证。 - ( 2 ) in order to overcome the difficulties caused by the non - minimum phase , an optimal ilc scheme with current feedback is presented for linear non - minimum phase plants based on noncausal stable inversion . the sufficient condition to ensure the convergence of this scheme is obtained and the utility mode of using the noncausal algorithm is given to fit the practical application
2 、针对迭代学习控制在非最小相位系统上应用效果差的缺点，根据非因果的稳定逆理论，提出了一种基于稳定逆的最优开闭环综合迭代学习控制，分析了学习律的收敛性并给出了这种非因果的学习律在实际应用中的运用方式。 - We focus our attention on the ilc architecture of using feedback and feedforward actions in order to improve the robustness of the ilc scheme . this dissertation aims to develop new methodologies for robust ilc design that involves a tradeoff between rapid convergence and good tracking performance . these design methods are systematic to resolve the problem of choosing the parameters in learning law and enhancing the utilitarian of ilc
为了加强算法的鲁棒性，重点采用同时具有反馈与前馈作用的开闭环综合迭代学习控制结构，旨在给出同时兼顾收敛性和跟踪性能的鲁棒迭代学习控制律的设计方法，避免学习律参数选择的盲目性，拓宽迭代学习控制的应用范围，加强迭代学习控制的实用性。 - Although conventional ilc provides a good tracking performance through a few trials by the simple input update law , the absence of proper guidelines to design an ilc controller and the weak robustness to disturbances , noise and initialization errors put obstacles in the application of ilc in practical situations
传统的迭代学习控制以其简单的算法形式和精确的跟踪效果引人注目，但也存在着学习律参数选择比较盲目和算法抗干扰能力不强的缺陷。而对于一个实用的控制技术，这两个问题都必须很好地解决。 - Discussion and testifying were made to the convergence of the algorithm under the condition of having constrains in objects " outputs . one new algorithm , which can maintain the convergent speed under such constraint conditions , was presented . using iterative learning control in real job of industrial robotic manipulators , we h
本文对对象输出有限制情况下迭代学习算法的收敛性做了讨论和证明，并且提出了一种在这种情况下，能相对维持收敛速率的迭代学习律的改进策略。