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

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.

Data‐driven approach to iterative learning control via convex optimisation

“…However, when is non‐minimum phase, this inversion is not possible as the learning filter then becomes unstable; in this case, there are methods in the literature that implement stable inversion approaches to build the learning function (see [33–35]). A stable inversion is necessary due to the fact that unstable filters can produce control actions that grow exponentially over time where, even over a finite duration, can become undesirably large (which can excite certain non‐linearities and even damage system components [36]). In either case, a model is required to build a learning function that best approximates the inverse of the plant dynamics.…”