Iterative feedback tuning (IFT) enables the data-based optimization of feedback controller parameters without the plant model and disturbance model. However, the traditional IFT suffers from a generally slow convergence rate and requires multiple experiments in each iteration, which severely limit its applicability for precision motion industry. In this paper, a new framework for IFT with its focus on the practical applicability is synthesized. Specifically, in order to improve the convergence rate, a novel two-loop iterative algorithm of IFT is proposed by introducing a weighted gradient of the performance criterion. This algorithm seeks to directly minimize the performance criterion instead of its linear approximation in each iteration. Furthermore, an unbiased estimate method of the auxiliary variables is developed based on the impulse response experiment. These guarantee the proposed approach high convergence rate with less experiments required per iteration. Comparative simulation and experimental results demonstrate that the proposed approach converges much faster than the traditional IFT, and outperforms the model-based approach in terms of the tracking performance. The ease of implementation and effectiveness makes the proposed approach highly suitable for industrial applications.

中文翻译：

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面向收敛率的迭代反馈调谐应用于超精密晶圆台

迭代反馈调整 (IFT) 能够在没有对象模型和干扰模型的情况下对反馈控制器参数进行基于数据的优化。然而，传统的IFT收敛速度普遍较慢，每次迭代需要多次实验，严重限制了其在精密运动行业的适用性。在本文中，综合了一个注重实际适用性的 IFT 新框架。具体而言，为了提高收敛速度，通过引入性能准则的加权梯度，提出了一种新颖的IFT双循环迭代算法。该算法寻求在每次迭代中直接最小化性能标准而不是其线性近似。此外，在脉冲响应实验的基础上，开发了一种辅助变量的无偏估计方法。这些保证了所提出的方法具有较高的收敛速度，每次迭代所需的实验较少。比较仿真和实验结果表明，所提出的方法比传统的 IFT 收敛得更快，并且在跟踪性能方面优于基于模型的方法。易于实施和有效使所提出的方法非常适合工业应用。并且在跟踪性能方面优于基于模型的方法。易于实施和有效使所提出的方法非常适合工业应用。并且在跟踪性能方面优于基于模型的方法。易于实施和有效使所提出的方法非常适合工业应用。