Multirate iterative learning control for enhanced motion performance with application to wafer scanner systems

Sun, Liting; Tomizuka, Masayoshi

doi:10.1109/isfa.2016.7790208

Cited by 2 publications

(1 citation statement)

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“…With the technological development of the semiconductor industry, manufacturers demand more precise performance from the wafer stage. To achieve the goal, researchers have developed many control strategies and applied them on the wafer stage, including iterative learning control (ILC) (Heertjes and Tso, 2007;Mishra and Tomizuka, 2009;Sun et al, 2014;Zheng et al, 2017), sliding mode control (SMC) (Heertjes and Verstappen, 2014;Li et al, 2016;WU et al, 2011;Ito et al, 2014), H∞ feedback control (van de Wal et al, 2002), multi-rate control (Sun and Tomizuka, 2016) and so on. Among them, SMC has attracted great attention for its simple implementation and robust performance in the presence of uncertainties and external disturbances (Fukushima et al, 2014;Kuang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Precise Motion Control of Wafer Stages via Adaptive Neural Network and Fractional-Order Super-Twisting Algorithm

Kuang

Sun

Gao

et al. 2021

Preprint

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To obtain precise motion control of wafer stages, an adaptive neural network and fractionalorder super-twisting control strategy is proposed. Based on sliding mode control (SMC), the proposed controller aims to address two challenges in SMC: 1) reducing the chattering phenomenon, and 2) attenuating the influence of model uncertainties and disturbances. For the first challenge, a fractionalorder terminal sliding mode surface and a super-twisting algorithm are integrated into the SMC design. To attenuate uncertainties and disturbances, an add-on control structure based on the radial basis function (RBF) neural network is introduced. Stability analysis of the closed-loop control system is provided. Finally, experiments on a wafer stage testbed system are conducted, which proves that the proposed controller can robustly improve the tracking performance in the presence of uncertainties and disturbances compared to conventional and previous controllers.

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