Model-Based ILC with a Modified Q-Filter for Complex Motion Systems: Practical Considerations and Experimental Verification on a Wafer Stage.

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.