Convergence Rate Oriented Iterative Feedback Tuning With Application to an Ultraprecision Wafer Stage

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.