Abstract
Iterative learning control (ILC) is one of the most popular tracking control methods for systems that repeatedly execute the same task. A system model is usually used in the analysis and design of ILC. Model-based ILC results in general in fast convergence and good performance. However, the model uncertainties and nonrepetitive disturbances hamper its practical applications. One of the commonly used solutions is the introduction of a low-pass filter, namely, the Q-filter. However, it is indicated in this paper that the existing Q-filter configurations compromise the servo performance, although improving the robustness. Motivated by the combination of performance and robustness, a novel Q-filter configuration in ILC is presented in this paper. Some practical considerations, such as the configuration of ILC in a feedback control system, the time delay compensation, and the learning coefficient, are provided in the implementation of the proposed ILC algorithm. The effectiveness and superiority of the proposed ILC versus existing Q-filter ILC are demonstrated by both theoretical analysis and experimental verification on a wafer stage.
Highlights
High-performance motion is typically required in many manufacturing environments [1,2,3,4] where a tool must track a prescribed reference trajectory with high speed as well as high accuracy
In the next-generation photolithography, the wafer stage is subject to tightening requirements on the servo performance due to larger throughput and smaller critical dimension [6]
In order to deal with the trade-off between servo performance and robustness against model uncertainties in the existing model-based Iterative learning control (ILC), a novel Q-filter configuration is proposed in this paper
Summary
High-performance motion is typically required in many manufacturing environments [1,2,3,4] where a tool must track a prescribed reference trajectory with high speed as well as high accuracy. The time-varying Q-filters in [22, 26, 27] and the nonlinear Q-filters in [28] extend the robustness and performance boundaries given by the fixed Q-filter in [16], the ILC algorithms in the form of (2) cannot converge to zero-tracking error unless Q q = 1 [24]. This motivates the following work in the paper:. The proposed algorithm provides improved performance (zero-tracking error) versus the Q-filter configuration in (2) while maintaining high robustness.
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