本文選題：光場(chǎng)式直線時(shí)柵　切入點(diǎn)：動(dòng)態(tài)測(cè)量　出處：《重慶理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：精密位移測(cè)量技術(shù)是一個(gè)國(guó)家制造業(yè)走向高、精、尖的基礎(chǔ)保障。隨著科學(xué)技術(shù)的快速發(fā)展,自動(dòng)化設(shè)備對(duì)精度要求的提高,精密動(dòng)態(tài)測(cè)量技術(shù)在現(xiàn)代科學(xué)技術(shù)領(lǐng)域已占有重要的地位。時(shí)柵是中國(guó)人自主發(fā)明的全新原理的位移傳感器,其中光場(chǎng)式直線時(shí)柵是時(shí)柵的另一個(gè)分支研究方向。目前,光場(chǎng)式直線時(shí)柵在靜態(tài)的測(cè)量實(shí)驗(yàn)中,精度為±2μm,速度在0~5mm/s的動(dòng)態(tài)測(cè)量實(shí)驗(yàn)條件下,精度為±8μm,但在速度大于5mm/s的實(shí)驗(yàn)中,測(cè)量精度有明顯的下降,具體表現(xiàn)在測(cè)量的位移量滯后于真實(shí)的位移量,即“時(shí)-空”不同步,并且在不同的測(cè)量速度下,分辨力發(fā)生變化,這兩個(gè)問(wèn)題是動(dòng)態(tài)誤差產(chǎn)生的主要原因。本文在國(guó)家自然科學(xué)基金項(xiàng)目“一種交變光場(chǎng)時(shí)空耦合的高速高精度位移傳感器研究”的資助下,針對(duì)傳感器的測(cè)量速度大于5mm/s動(dòng)態(tài)測(cè)量中存在的兩個(gè)問(wèn)題,開(kāi)展了高精度的動(dòng)態(tài)測(cè)量方法的研究。研究目標(biāo):研究?jī)煞N提高光場(chǎng)式直線時(shí)柵動(dòng)態(tài)特性的方法,以期在5mm/s以上動(dòng)態(tài)測(cè)量實(shí)驗(yàn)中,傳感器的測(cè)量精度做到±4μm以內(nèi)。首先,提出了提高光場(chǎng)式直線時(shí)柵動(dòng)態(tài)性能的兩種方法—BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法、連續(xù)動(dòng)態(tài)比相法,對(duì)這兩種方法建立了相應(yīng)的數(shù)學(xué)模型,結(jié)合時(shí)柵傳感器的工作原理,闡述了兩種方法的原理與可行性分析。其次,設(shè)計(jì)出了兩種方法的硬件電路原理圖和相應(yīng)的PCB電路板,其中BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法的硬件電路是以FPGA為核心,主要包括激勵(lì)信號(hào)模塊、雙通道A/D采集模塊、行波信號(hào)合成電路、UART通信模塊等。連續(xù)動(dòng)態(tài)比相法的硬件電路設(shè)計(jì)主要包括A/D采集模塊、D/A轉(zhuǎn)換模塊、雙向比相電路等,以FPGA作為微處理器對(duì)各模塊作邏輯運(yùn)算。最后,搭建了實(shí)驗(yàn)平臺(tái),對(duì)以上兩種方法進(jìn)行動(dòng)態(tài)標(biāo)定和實(shí)驗(yàn)驗(yàn)證,在加速度接近于0mm?,速度在9mm/s的近似勻速運(yùn)動(dòng)狀態(tài)下,BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法位移的預(yù)測(cè)誤差峰-峰值為±4μm,連續(xù)動(dòng)態(tài)比相法的誤差峰-峰值為±3μm。在加速度大小為4mm?至5mm?時(shí),BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法的誤差峰-峰值為±9μm,連續(xù)動(dòng)態(tài)比相法的誤差峰-峰值為±6μm。實(shí)驗(yàn)結(jié)果表明:兩種方法都實(shí)現(xiàn)了預(yù)期的研究目標(biāo),但在加速度較大的測(cè)量條件下,BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)算法與連續(xù)動(dòng)態(tài)比相法的測(cè)量精度都有所下降。
[Abstract]:Precision displacement measurement technology is the basic guarantee of high, fine and sharp manufacturing industry in a country. With the rapid development of science and technology, the requirement of precision for automation equipment is improved. Precision dynamic measurement technology has played an important role in the field of modern science and technology. The time-grating is a new principle displacement sensor invented by Chinese people, among which the light-field linear time-grating is another branch of the time-grating. In the static measurement experiment, the accuracy of the light field straight line grating is 鹵2 渭 m, and the accuracy is 鹵8 渭 m when the velocity is 0 ~ 5 mm / s, but in the experiment with the velocity greater than 5 mm / s, the measurement accuracy is obviously decreased. The measured displacement lags behind the real displacement, that is, "time-space" is out of sync, and the resolution changes at different measuring speeds. These two problems are the main causes of the dynamic error. This paper is funded by the National Natural Science Foundation of China, a high-speed and high-precision displacement sensor with space-time coupling of alternating light fields. In view of the two problems existing in the dynamic measurement of sensor measuring speed greater than 5mm / s, the research of high precision dynamic measurement method is carried out. Objective: to study two methods to improve the dynamic characteristics of light field linear grating. It is expected that in the dynamic measurement experiment above 5 mm / s, the measurement accuracy of the sensor is within 鹵4 渭 m. Firstly, two methods to improve the dynamic performance of light field straight line gate, the BP neural network prediction algorithm, the continuous dynamic phase comparison method, are proposed. The corresponding mathematical models of the two methods are established, and the principle and feasibility analysis of the two methods are expounded in combination with the working principle of the time grating sensor. Secondly, the hardware circuit schematic diagram and the corresponding PCB circuit board of the two methods are designed. The hardware circuit of BP neural network prediction algorithm is based on FPGA, including excitation signal module, dual channel A / D acquisition module. The hardware circuit design of continuous dynamic phase comparison method mainly includes A / D acquisition module / D / A conversion module, bidirectional phase comparison circuit and so on. The FPGA is used as microprocessor to perform logic operation on each module. The experimental platform is built to calibrate and verify the above two methods dynamically, and the acceleration is close to 0mm? The prediction error peak to peak value of displacement of BP neural network is 鹵4 渭 m, the error peak of continuous dynamic phase comparison method is 鹵3 渭 m, and the error peak of continuous dynamic phase comparison method is 鹵3 渭 m under the condition of approximately uniform velocity motion of 9 mm / s, the prediction error peak to peak value of BP neural network is 鹵4 渭 m, and the magnitude of acceleration is 4 mm? To 5mm? The error peak to peak value of BP neural network prediction algorithm is 鹵9 渭 m, and the error peak to peak value of continuous dynamic phase comparison method is 鹵6 渭 m. The experimental results show that both methods achieve the expected research goal. However, the accuracy of BP neural network prediction algorithm and continuous dynamic phase comparison method are decreased under the condition of high acceleration.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP212;TP183

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