Model and experience-based initial input construction for iterative learning control
Model and experience-based initial input construction for iterative learning control
复制标题
用于迭代学习控制的模型和基于经验的初始输入构建
DOI：
10.1002/acs.1209
复制
发表时间：
2010
影响因子：
3.1
通讯作者：
Freeman C
中科院分区：
文献类型：
--
作者：
Freeman C
迭代学习控制(ILC)中初始输入的选择通常对后续试验产生的误差有显著影响。在本文中，开发了使用在ILC之前的应用中收集的实验数据来产生用于跟踪新的参考轨迹的初始输入信号的技术。然后结合基于模型的方法来克服先前实验数据不足的限制，并开发了稳健的设计程序。实验评估结果是使用龙门机器人设施获得的。版权所有©2010 John Wiley&Sons，Ltd.
The initial choice of input in iterative learning control (ILC) generally has a significant effect on the error incurred over subsequent trials. In this paper, techniques are developed that use experimental data gathered over previous applications of ILC in order to generate an initial input signal for the tracking of a new reference trajectory. A model‐based approach is then incorporated to overcome the limitation of insufficient previous experimental data, and a robust design procedure is developed. Experimental evaluation results are obtained using a gantry robot facility. Copyright © 2010 John Wiley & Sons, Ltd.