光栅刻划机300mm行程工作台研制及其自适应控制方法  

300 mm-Travel Stage of Grating Ruling Engine and Its Self-Adaptive Control Method

作　　者：李晓天[1] 于海利[1] 齐向东[1] 朱继伟[1] 于宏柱[1] 巴音贺希格[1] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033

出　　处：《中国激光》2014年第6期169-176,共8页Chinese Journal of Lasers

基　　金：国家973计划(2014CB049500);国家重大科研装备研制项目(61227901;ZDYZ2008-1);吉林省重大科技攻关项目(09ZDGG005)

摘　　要：光栅刻划机工作台性能及其控制算法是直接影响大面积光栅刻划精度的重要原因。为了提高光栅刻划机运行精度,研制了采用压电陶瓷进行微定位控制的300mm行程宏微两级工作台,建立了微定位工作台数学模型,仿真分析了工作台参数对系统动态性能的影响。采用反向传播(BP)神经网络比例-积分-微分(PID)算法对微定位工作台进行闭环控制。仿真分析表明通过增大内外台连接刚度或内外台之间阻尼在总体趋势上均可改善与光栅质量密切相关的微定位工作台动态性能。工作台定位实验表明,在以双频激光干涉仪为纳米位移测量基准的情况下,进行刻线密度为35line/mm以上常用光栅空运刻划时,BP神经网络PID算法可实现宏微两级工作台定位误差3σ值不大于5.0nm。以上研究为大尺寸光栅刻划机宏微两级工作台结构设计及控制算法的选择提供了理论及技术指导。Stage properties and its control algorithms of grating-ruling engine are important reasons that directly influence the ruling accuracy of large grating.In order to improve the running accuracy of grating ruling engine,a300 mm travel macro-micro stage using PZT as actuator of the micro positioning stage is developed.Mathematical model of micro-positioning stage is established,and the influence of stage′s parameters on the dynamic performance of the system is analyzed by simulation.Micro-positioning stage is closed-loop controlled by BP neural network proportion integration differentiation（PID）method.Simulation results show that the dynamic performance of micropositioning stage which is closely related with grating quality can be improved in general trend by increasing the stiffness or damping between inside and outside stage.Positioning experiments of stage,which is used to emptily rule35 line/mm or higher line density grating and employ dual-frequency laser interferometer as nano-positioning measurement standard,show that BP neural network PID algorithm can achieve 3σpositioning error of macro-micro stage to be no more than 5.0nm.The studies above can provide theoretical and technical guidance for structure design and control-algorithm choice of macro-micro stage of large grating ruling engine.

关 键 词：光栅 光栅刻划机 宏微两级工作台 BP神经网络 PID控制 自适应控制 

分 类 号：O433.4[机械工程—光学工程]