• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE/ASME International Conference on Advanced Intelligent Mechatronics >Segmented Iterative Learning Control for Precision Positioning of Waferstages
【24h】

Segmented Iterative Learning Control for Precision Positioning of Waferstages

机译：用于瓦夫斯特格精确定位的分段迭代学习控制

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents an application of a segmented iterative learning control (ILC) scheme for precision positoning of waferstages using linear motors. The process of wafer scanning is repetitive, hence ILC has been used to improve performance from cycle to cycle. However, using uniform learning over the entire cycle results in degraded performance in those areas of the trajectory where nonrepetitive disturbances dominate. The paper proposes and compares implementation and performance of a segmented ILC scheme (a segmented P-type ILC algorithm) vis-a-vis a standard P-type ILC algorithm. Segmentation refers to separating the repetitive process into smaller segments with different learning rates. In this paper, we first consider the simple case of on-off learning, i.e., in certain segments learning is switched on, and in others, no learning is done. A metric is proposed to determine which segments to learn, and sufficient conditions for convergence and stability aspects of an on-off type segmented ILC scheme are presented. Finally, a comparison of the segmented ILC scheme and the standard P-type scheme is provided to illustrate the advantages of segmentation. Experimental validation of the theory developed is provided by implementation of these ILC schemes on a one-DOF prototype waferstage.
机译：本文介绍了分段迭代学习控制（ILC）方案，用于使用线性电动机的Waferstages精确阳性。晶片扫描过程是重复的，因此ILC已被用于从循环到循环的性能。然而，在整个循环中使用统一学习导致非重复障碍占据主导地位的轨迹的那些区域中的降级性能。本文提出并比较了分段ILC方案的实施和性能（分段的p型ILC算法）VIS-A-VIS标准P型ILC算法。分割是指利用不同学习速率将重复过程分离成较小的段。在本文中，我们首先考虑开关学习的简单情况，即，在某些细分学习中，在其他方面，在其他方面，没有学习。提出了一种度量来确定要提出的学习的哪个段，以及足够的用于接通类型分段ILC方案的收敛和稳定性方面的段。最后，提供了分段ILC方案和标准P型方案的比较以说明分割的优点。通过在一流的原型Waferstage上实施这些ILC计划提供了该理论的实验验证。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号