基于自适应神经网络和分数阶超扭转算法的硅片台精密运动控制
Precise Motion Control of Wafer Stages via Adaptive Neural Network and Fractional-Order Super-Twisting Algorithm
摘要：
为了实现硅片台的精确运动控制，提出了一种自适应神经网络和分数阶超扭转控制策略。该控制器基于滑模控制（SMC），旨在解决滑模控制中的两个难题：1）减小抖振现象; 2）减弱模型不确定性和干扰的影响。对于第一个挑战，分数阶终端滑模面和超扭曲算法集成到SMC设计。为了抑制不确定性和干扰，引入了一种基于径向基函数（RBF）神经网络的附加控制结构。给出了闭环控制系统的稳定性分析。最后，在硅片台实验台系统上进行了实验，结果表明，与传统的控制器相比，所提出的控制器可以鲁棒地提高跟踪性能的不确定性和干扰的存在。
英文摘要：
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.
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