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Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System

机译:晶圆平台系统的鲁棒控制的约束迭代反馈调整

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Iterative feedback tuning (IFT) enables the data-driven tuning of controller parameters without the explicit need for a parametric model. It is known, however, that IFT can lead to nonrobust solutions. The aim of this paper is to develop an IFT approach with robustness constraints. A constrained IFT problem is formulated that is solved by introducing a penalty function. Essentially, the gradient estimates decompose into: 1) the well-known IFT gradients and 2) the gradients with respect to this penalty function. The latter are obtained through a nonparametric model of the controlled system. This guarantees robust stability while only requiring a nonparametric model. The experimental results obtained from the motion control systems of an industrial wafer scanner confirm enhanced performance with guaranteed robustness estimates.
机译:迭代反馈调整(IFT)使得控制器参数的数据驱动式调整无需明确的参数模型即可。但是,众所周知,IFT可能导致不可靠的解决方案。本文的目的是开发一种具有鲁棒性约束的IFT方法。提出了一个受约束的IFT问题,该问题可以通过引入罚函数来解决。本质上,梯度估计分解为:1)众所周知的IFT梯度; 2)关于此惩罚函数的梯度。后者是通过受控系统的非参数模型获得的。这保证了鲁棒的稳定性,同时仅需要非参数模型。从工业晶圆扫描仪的运动控制系统获得的实验结果证实了增强的性能并保证了鲁棒性。

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