Learning in the synthesis of data-driven variable-gain controllers

被引：0

作者：

Heertjes, Marcel ^{[1
]}

Hunnekens, Bram ^{[1
]}

van de Wouw, Nathan ^{[1
]}

Nijmeijer, Henk ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands

来源：

2013 AMERICAN CONTROL CONFERENCE (ACC) | 2013年

关键词：

data-driven optimization; gradient methods; Lur'e systems; motion control; self-tuning; wafer scanners; PERFORMANCE; STABILITY; FEEDBACK; DESIGN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To deal with performance trade-offs in the control of motion systems, a method is developed for designing variable-gain feedback controllers. The idea is to select a piecewise affine controller structure and, subsequently, to find the nonlinear controller parameter values of this structure by data-driven performance optimization. Herein an H-2 performance objective is minimized. As a result, variable-gain controllers are synthesized using techniques from the field of learning and optimization. The method is applied to a wafer stage simulation model.

引用

页码：6685 / 6690

页数：6

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