【摘要】：當(dāng)今時(shí)代IC已經(jīng)成為全球電子信息行業(yè)的核心,而其中光刻機(jī)是關(guān)鍵設(shè)備之一。本文針對(duì)光刻機(jī)雙工件臺(tái)中直線電機(jī)重復(fù)周期性運(yùn)行的特點(diǎn)及系統(tǒng)對(duì)光刻機(jī)性能的要求,對(duì)電機(jī)的精密控制問題進(jìn)行了研究。提出了基于模糊策略的增益自整定迭代學(xué)習(xí)控制方法,且基于遺傳算法對(duì)其進(jìn)行了優(yōu)化,并驗(yàn)證了算法的有效性。首先,分析了光刻機(jī)系統(tǒng)的整體情況以及雙工件臺(tái)系統(tǒng)的結(jié)構(gòu)和功能,根據(jù)具體的掃描曝光過程流程及各電機(jī)在此過程中所承擔(dān)的功能,設(shè)計(jì)了電機(jī)運(yùn)行的三階S曲線。此外,計(jì)算得到了本課題中所研究的永磁直線電機(jī)基于PARK變換和矢量控制的數(shù)學(xué)模型。其次,根據(jù)電機(jī)周期性運(yùn)行的特點(diǎn)及性能要求,將迭代學(xué)習(xí)控制引入到雙工件臺(tái)直線電機(jī)的控制當(dāng)中,經(jīng)過數(shù)次迭代后可以明顯提高精度,但收斂速度還有提升的余地。因此,本文提出了一種基于模糊策略的增益自調(diào)整迭代學(xué)習(xí)控制,即利用Mamdani型模糊控制器調(diào)整迭代學(xué)習(xí)的增益,該方法可以在不太影響精度的同時(shí),大幅提高收斂速度。但該控制方法在仿真中出現(xiàn)了震蕩的現(xiàn)象,其控制效果有很大的改進(jìn)余地,模糊控制規(guī)則還可以進(jìn)一步優(yōu)化。針對(duì)上述問題,本文應(yīng)用遺傳算法優(yōu)化模糊規(guī)則,建立模糊迭代學(xué)習(xí)控制方法。即利用遺傳算法優(yōu)化了模糊規(guī)則后件中的十個(gè)參數(shù),經(jīng)過若干代的進(jìn)化之后,產(chǎn)生一組最優(yōu)解,將該最優(yōu)解對(duì)應(yīng)的模糊規(guī)則用于Mamadani型模糊控制器,利用模糊控制器的輸出調(diào)整迭代學(xué)習(xí)的增益,并進(jìn)行仿真分析。仿真結(jié)果表明該方法穩(wěn)定性很好,改善了專家經(jīng)驗(yàn)型模糊迭代學(xué)習(xí)控制中出現(xiàn)的振蕩現(xiàn)象。收斂速度也與專家經(jīng)驗(yàn)的模糊迭代學(xué)習(xí)控制相近,且最終的控制精度也有一定的提高,說明了算法的有效性。此外,本文還對(duì)模糊迭代學(xué)習(xí)控制的抗干擾能力進(jìn)行了仿真分析,優(yōu)化后的模糊迭代學(xué)習(xí)控制在干擾下,也能取得良好的控制效果。最后,本文對(duì)X向直線電機(jī)設(shè)計(jì)了三組實(shí)驗(yàn),普通的迭代學(xué)習(xí)控制和兩組基于模糊策略的迭代學(xué)習(xí)控制。通過實(shí)驗(yàn)可以得出以下規(guī)律:普通的迭代學(xué)習(xí)控制可以在收斂速度很慢的條件下保證很高的精度,但收斂速度很難提高,而基于模糊策略的迭代學(xué)習(xí)控制可以大幅度地提高收斂速度。尤其是基于GA優(yōu)化算法改進(jìn)的模糊迭代學(xué)習(xí)控制方法,達(dá)到了收斂速度和控制精度完美的結(jié)合,找到了一個(gè)平衡點(diǎn),可以應(yīng)用于重復(fù)運(yùn)行某一曲線的直線電機(jī)的控制上,例如本實(shí)驗(yàn)室光刻機(jī)雙工件臺(tái)的直線電機(jī)�？梢允盏綕M意的控制效果。
[Abstract]:Nowadays, IC has become the core of the global electronic information industry, and lithography machine is one of the key equipment. In this paper, the precision control of the linear motor is studied according to the characteristics of repeated periodic operation of the linear motor in the double workpiece platform of the lithography machine and the requirements of the system for the performance of the lithography machine. A gain self-tuning iterative learning control method based on fuzzy strategy is proposed and optimized based on genetic algorithm, and the effectiveness of the algorithm is verified. Firstly, the overall situation of the lithography machine system and the structure and function of the duplex system are analyzed. According to the specific scanning and exposure process flow and the functions of each motor in this process, the third-order S curve of the motor operation is designed. In addition, the mathematical model of permanent magnet linear motor based on PARK transform and vector control is obtained. Secondly, according to the characteristics and performance requirements of the periodic operation of the motor, the iterative learning control is introduced into the control of the linear motor with two workpieces. After several iterations, the accuracy can be improved obviously, but the convergence speed still has room to improve. Therefore, in this paper, a gain self-tuning iterative learning control based on fuzzy strategy is proposed, that is, Mamdani fuzzy controller is used to adjust the gain of iterative learning. This method can greatly improve the convergence speed without much affecting the accuracy. However, the control method has the phenomenon of concussion in the simulation, its control effect has a lot of room for improvement, and the fuzzy control rules can be further optimized. In order to solve the above problems, genetic algorithm is used to optimize fuzzy rules and fuzzy iterative learning control method is established in this paper. That is, the genetic algorithm is used to optimize the ten parameters in the latter part of the fuzzy rule. After several generations of evolution, a set of optimal solutions is generated, and the fuzzy rules corresponding to the optimal solution are applied to the Mamadani fuzzy controller. The output of fuzzy controller is used to adjust the gain of iterative learning, and the simulation analysis is carried out. The simulation results show that the method is stable and improves the oscillations in expert empirical fuzzy iterative learning control. The convergence rate is also similar to the fuzzy iterative learning control with expert experience, and the final control accuracy is also improved to a certain extent, which shows the effectiveness of the algorithm. In addition, the anti-interference ability of fuzzy iterative learning control is simulated and analyzed in this paper. The optimized fuzzy iterative learning control can also achieve good control effect under interference. Finally, three groups of experiments, ordinary iterative learning control and two groups of iterative learning control based on fuzzy strategy, are designed for X-direction linear motor. Through experiments, the following laws can be obtained: ordinary iterative learning control can ensure high accuracy under the condition of slow convergence speed, but the convergence speed is difficult to improve. The iterative learning control based on fuzzy strategy can greatly improve the convergence speed. In particular, the improved fuzzy iterative learning control method based on GA optimization algorithm achieves the perfect combination of convergence speed and control accuracy, and finds a balance point, which can be applied to the control of linear motor running a certain curve repeatedly. For example, the linear motor of the double workpiece table of the lithography machine in our laboratory. Satisfactory control effect can be obtained.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

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本文編號(hào)：2496087