【摘要】：隨著生產(chǎn)實踐的進一步發(fā)展,對于測角系統(tǒng)的精度和穩(wěn)定性等提出了更高的要求。基于Renishaw增量式圓光柵的高精度測角系統(tǒng),本課題研究采用多讀數(shù)頭結(jié)構(gòu)和軟件補償算法提高系統(tǒng)的測角精度�；谡`差建模,分析多讀數(shù)頭結(jié)構(gòu)的消除誤差原理和應(yīng)用場景,從原理和仿真實驗角度對比分析了數(shù)值補償算法、BP網(wǎng)絡(luò)補償以及基于小波神經(jīng)網(wǎng)絡(luò)(WNN)的誤差回溯補償三類補償算法對于系統(tǒng)的補償效果及其適用場景。通過圓光柵測角系統(tǒng)誤差的建模,定量分析了系統(tǒng)誤差、隨機誤差和安裝誤差的組成成分,推導(dǎo)了多讀數(shù)頭結(jié)構(gòu)消除誤差原理,定量地分析了安裝傾斜和安裝不對徑下多讀數(shù)頭的誤差的消除效果。分析了分段線性、三次樣條插值、多項式以及諧波補償四種數(shù)值補償算法,并且對離散標定的測角誤差進行了兩次迭代補償實驗,對比分析四種算法補償效果和適用性,其中諧波補償測角誤差補償效果最好。分析了BP神經(jīng)網(wǎng)絡(luò)算法原理和方法,通過改變訓(xùn)練算法和調(diào)整網(wǎng)絡(luò)結(jié)構(gòu)改進了BP網(wǎng)絡(luò),并且對誤差進行兩次迭代實驗,對比分析了其與數(shù)值補償算法的補償效果。為了優(yōu)化系統(tǒng)設(shè)計,研究了基于小波神經(jīng)網(wǎng)絡(luò)的誤差溯源補償方法,并且采用小波網(wǎng)絡(luò)方法對圓光柵測角誤差進行溯源補償,比較了其狹義和廣義小波網(wǎng)絡(luò)兩種算法的補償效果,實驗驗證了小波網(wǎng)絡(luò)算法、數(shù)值補償算法、BP網(wǎng)絡(luò)算法三種算法的補償有效性以及分析了它們的補償效果和適用場合。狹義小波網(wǎng)絡(luò)兼具神經(jīng)網(wǎng)絡(luò)和小波變換的特性,補償效果比BP網(wǎng)絡(luò)好;通過實驗比較廣義小波網(wǎng)絡(luò)和諧波補償算法,廣義小波網(wǎng)絡(luò)能辨識出來附加誤差成分,因此達到的誤差補償效果比諧波補償好,適合動態(tài)誤差的補償。
[Abstract]:With the further development of production practice, higher requirements for accuracy and stability of angle measuring system are put forward. Based on the high precision angle measurement system of Renishaw incremental circular grating, this paper studies how to improve the angle measurement accuracy of the system by using multi-readout head structure and software compensation algorithm. Based on error modeling, the error elimination principle and application scenario of multi-reading head structure are analyzed. From the point of view of principle and simulation experiment, the compensation effect of numerical compensation algorithm (BP) and error backtracking compensation based on wavelet neural network (WNN) for the system and its applicable scenarios are compared and analyzed. By modeling the error of circular grating angle measuring system, the components of system error, random error and installation error are quantitatively analyzed, and the principle of eliminating error by multi-reading head structure is deduced. The effect of eliminating the error of multiple reading heads under the installation of tilting and misalignment is quantitatively analyzed. Four numerical compensation algorithms, piecewise linear, cubic spline interpolation, polynomial and harmonic compensation, are analyzed, and two iterative compensation experiments are carried out for the angle measurement error of discrete calibration. The compensation effect and applicability of the four algorithms are compared and analyzed. The compensation effect of harmonic compensation for angle measurement error is the best. The principle and method of BP neural network algorithm are analyzed, the BP network is improved by changing the training algorithm and adjusting the network structure, and the error is tested twice, and the compensation effect of the algorithm is compared with that of the numerical compensation algorithm. In order to optimize the system design, the compensation method of error traceability based on wavelet neural network is studied. The compensation effects of the two algorithms are compared. The experimental results show that the compensation effectiveness of the wavelet network algorithm and the numerical compensation algorithm / BP network algorithm are verified and their compensation effect and application situation are analyzed. The narrow wavelet network has the characteristics of neural network and wavelet transform, and the compensation effect is better than that of BP network. By comparing the generalized wavelet network with harmonic compensation algorithm, the generalized wavelet network can identify the additional error component. Therefore, the error compensation effect is better than harmonic compensation, which is suitable for dynamic error compensation.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TH741.2

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