Intermittent iterative learning control

Intermittent iterative learning control
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DOI：
10.1109/cacsd-cca-isic.2006.4776753
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发表时间：
2006-10
期刊：
2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control
影响因子：
--
通讯作者：
H. Ahn;Y. Chen;Kevin L. Moore
H. Ahn;Y. Chen;Kevin L. Moore
中科院分区：
其他
文献类型：
--
作者：
H. Ahn;Y. Chen;Kevin L. Moore

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在本文中，我们提出了当系统遭受数据丢失时鲁棒迭代学习控制（ILC）设计问题的数学公式。假设ILC方案通过网络控制系统（NCS）实现，并且在从远程工厂到ILC控制器的数据传输过程中发生数据丢失，导致我们称之为间歇性测量。使用卡尔曼滤波方法，我们表明，只要没有完全的数据丢失，就有可能设计一个学习增益，使系统最终收敛到期望的轨迹
In this paper, we present a mathematical formulation of the problem of robust iterative learning control (ILC) design when the system is subject to data dropout. It is assumed that an ILC scheme is implemented via a networked control system (NCS) and that during the data transfer from the remote plant to the ILC controller data dropout occurs, resulting in what we call intermittent measurement. Using the Kalman filtering approach, we show that it is possible to design a learning gain such that the system eventually converges to a desired trajectory as long as there is not complete data dropout