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

TL;DR

This paper introduces a novel adaptive neural network and fractional-order super-twisting control strategy to enhance the precision and robustness of wafer stage motion control, effectively reducing chattering and mitigating uncertainties.

Contribution

It combines fractional-order sliding mode control with neural network-based adaptive compensation for the first time in wafer stage control.

Findings

Significantly reduced chattering in control signals.

Improved tracking accuracy under uncertainties.

Robust performance demonstrated through experiments.

Abstract

To obtain precise motion control of wafer stages, an adaptive neural network and fractional-order 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 fractional-order 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.