Research On Control Technologies Of The Macroscopic Scale And Nano Positioning

Recently,asthedevelopmentofnumerousindustrialareassuchasmicro-machines,ultra-precision measurement, ultra-precision machining, integrated circuit manufactur-ing, biotechnology, medical science, optical docking and robotics, the equipment withlarger positioning range and higher positioning accuracy are highly required, so themacroscopic-scale nano-precision positioning technology has been a hot in engineeringresearch field. Meanwhile, as the development of large-scale astrophysics project, therequirement on high-precision diffraction gratings in large diameter grows. Therefore,by taking the indexing and positioning system in dissertation diffraction grating rulingmachine as the research object, this dissertation investigated the system construction,system modeling and characterization, positioning error compensation control strategyand macro/micro-dual positioning in macroscopic-scale and nano-positioning technol-ogy.By comparing the operating mode and system component of diffraction gratingruling engine with that of other macroscopic-scale and nano-positioning systems, aclosed loop macroscopic-scale nano-positioning indexing and positioning system withmacro/micro dual-drive is established, with the DC brushless torque motor plus wormgear and screw nut mechanism as macro blank, with the piezoelectric ceramics plus s-teel spring asmicroblank, with thedual-frequency laser interferometerasmeasurementequipment. Through simulating the indexing positioning and ruling systems of the d-iffraction grating ruling engine on PC via VC, effective control on diffraction gratingruling engine No. 2 in Changchun Institute of Optics, Fine Mechanics and Physics ofChinese Academy of Sciences is realized. In practice, the average 3σpositioning errorof the indexing positioning system is less than 7nm within 300m positioning range.Inthesystemmodelingofthemacro/microdual-drivepositioningsystem, bycom-paring the advantages and shortcomings of the commonly used"white box modeling","black box modeling"and"gray box modeling", the"gray box modeling"is adopted.According to the link mode of the macro and micro worktable, overall dynamic modelof different driving modes (Macro drive single input - dual output, Micro drive singleinput - dual output, and macro / micro drive dual input - dual output)are establishedby mechanism modeling. Through system identification experiments, unknown param-eters in systems are estimated and system model close to the actual system is obtained.Dynamiccharacterofthesystemisanalyzedbased on"graybox modeling", andfurther improving methods are proposed.ThroughanalyzingthetraditionalPIDcontroltheoryandexperimentalresults, thisdissertation stated it is not suitable for macro-scale and nano-positioning system controland the reasons are given. Based on the principles of neural network, the advantage ofusing neural network PID control in macro-scale nano-positioning system is demon-strated. By analyzing control algorithm of the single neuron PID control and BP neuralnetwork PID control, together with the simulation of the system model based on"graybox modeling", the performance of the two types of PID control algorithm were com-pared.Finally, according to the positioning accuracy demand of the mechanical structureandindexingsystemofdiffractiongratingrulingengine, severalkeydriversandsensorsof the positioning system were selected and the experiment platform was constructed.Based on this, the computer-controlled software of the grating ruling engine was de-veloped with the virtual hardware in the loop method, and the positioning process andruling process of the ruling engine were realized in a fully virtual environment usingcontrol system model based on"gray box modeling". Through the step positioningexperiments on the single neuron PID and BP neural network PID control algorithm,the two control algorithm were simulated and verified, and the rapidity and accuracyof the control algorithms was compared. It is obtained that BP neural network controlconverges faster, is more adaptable to changes in the system, works faster, can achievehigher accuracy, but has larger overshoot. Then, based on the working principle ofdiffraction grating ruling engine, intermittent positioning and macro worktable contin-uous mode-micro worktable intermittent mode positioning control of the grating rulingengine were realized. Ruling experiment were conducted and the analysis on the result-s show that the macro positioning system of the diffraction grating ruling engine No.2 in Changchun Institute of Optics, Fine Mechanics and Physics of Chinese Academyof Sciences has crawl, therefore, by using intermittent positioning and fast BP neuralnetwork PID control algorithm, high accuracy (7nm or less)can be achieved, whilethe accuracy performance of the macro worktable continuous mode-micro worktableintermittent mode positioning is poor. The correctness of the work in the dissertation isverified by micro-positioning experiments and practical ruling experiments.