Batch-to-Batch Rational Feedforward Control: From Iterative Learning to Identification Approaches, With Application to a Wafer Stage

Abstract

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Iterative machine learning for precision trajectory tracking with series elastic actuators

“…The iteration approach allows large changes in input updates in frequency regions where the model uncertaintly is small. No changes or small changes in the in the input updates are used if the predicted model uncertainty is large [25]- [27].…”

Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

“…For an application to the wafer stage of Fig. 1(a), see (102) . The theoretical framework is further extended towards input shaping (103) and rational feedforward structures in (104)- (106) , which have as key advantage that these can exactly compensate non-minimum phase dynamics, see (107) for a detailed exposition, and also (95) (108) (109) for further details and applications.…”

“…Based on measured sensor information, the controller predicts the spatio-temporal behavior (either locally at the performance location or globally), and determines a suitable force profile for accurately positioning the stage and its internal flexible dynamics for identification of models for feedforward, where the answer lies in identifying the inverse system directly using instrumental variable system identification (112) . For a detailed comparison to ILC-based approaches, see (102) . Notice that in ILC, the identification criterion for the model, which is needed to construct the learning filter, is slightly different and should be chosen such that the model error is less than 100% in the relevant frequency range of interest, see (93) for details in this direction.…”

Exact and Causal Inversion of Nonminimum-Phase Systems: A Squaring-Down Approach for Overactuated Systems

“…System inversion is at the heart of achieving high performance in many control applications, including printing systems [1], atomic force microscopes [2], and wafer stages [3]. It is extensively used in, for example, inverse model feedforward [4] and iterative learning control (ILC) [5].…”