Abstract：This paper presents a robust adaptive neural network tracking compensation control strategy based on permanent magnetic linear synchronous motor (PMLSM) for long-stroke reticle stage of lithography. It can estimate the model uncertainty and external nonlinear disturbance real-time online by radial basis function (RBF) neural network. A long-stroke PMLSM model of reticle stage based on parametric uncertainty and external disturbance was established. The derivation of the control strategy and the theoretical stability were analyzed. It was shown that the proposed model can guarantee convergence of the position error and velocity error. The actual effectiveness of this control strategy was verified by a fifth-order S-curve tracking experiment on the long-stroke reticle stage of lithography. The experimental data showed that the tracking accuracy met the design requirements well. This strategy doesn’t require precise modeling of the actual system parameters and the external disturbances which are difficult to measure. It is very suitable for the application in precision motion control field.
王一光, 李晓杰, 陈兴林. 基于永磁直线同步电机的光刻机掩模台鲁棒自适应神经网络控制[J]. 电工技术学报, 2016, 31(6): 38-46.
Wang Yiguang, Li Xiaojie, Chen Xinglin. A Robust Adaptive Neural Network Control Method Based on Permanent Magnetic Linear Synchronous Motor for the Reticle Stage of Lithography. Transactions of China Electrotechnical Society, 2016, 31(6): 38-46.
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