臺灣博碩士論文加值系統

　　在工業4.0的推廣下，工業製造智能化為必然的趨勢，智能化有許多面向，但大體上都是以提升加工精度為目的，迭代學習控制便是其中一種智能化的方法，在迭代學習控制中，大部分皆是以改善追蹤誤差為提升精度的方法，然而實際影響工件精度的是輪廓誤差，但由於輪廓誤差不好量測，且在大部分的文獻中皆以近似的方法來估算輪廓誤差，此舉會使精度較差，因此本研究提出以等效輪廓誤差之概念進行輪廓上的補償。
　　本研究提出以核回歸為基礎來計算輸出軌跡之等效輪廓誤差，其方法為將命令軌跡曲線以高斯函數為基底，經由非線性轉換至一高維空間，使該曲線在高維空間下為一超平面，並以此超平面為基準計算輸出位置在高維空間下的輪廓誤差與其梯度向量，再將等效輪廓誤差與其梯度向量結合P-type迭代學習控制方法進行輪廓誤差之補償，以達到高精度與智能化之目標。
　　本研究可應用於各種不同的多軸工具機或是機器手臂，由數值模擬與實驗驗證結果可以證實，在以本研究所提出之等效輪廓誤差學習控制方法應用於XY-Table和Delta機器人上進行輪廓補償後，確實能夠有效的降低輪廓誤差，且無論是在直角坐標或是其他座標系下皆可以進行補償，這顯示了本研究所提出之方法的可行性及廣泛性。

　　With the “Industry 4.0” promoted by government, traditional industry being transformed to intelligent manufacturing is a tendency. There are a lot of methods to make manufacturing intelligence. In most cases of motion control in manufacturing, the target is to improve the product’s accuracy. The iterative learning control is the one of methods to the aim of intelligent manufacturing. In the past, improving tracking error is most commonly used in iterative learning control, however improving contour error is more useful to increase the product’s accuracy. Although the contour error is the key of accuracy, most of methods cannot easily achieve good performance due to the lack of description or calculation of contour error, especially in high dimensional space. Therefore, the research had a new idea of improving contouring control using the concept of equivalent contour error.
　　Based on kernel regression to describe the output’s equivalent contour error, we use Gaussian function to transform the curve to the multidimensional space. Then, make the curve be a hyperplane in the multidimensional space. After that, calculate the equivalent contour error and the gradient vector in the hyperplane. Finally, the equivalent contour error and gradient vector are combined with the P-type iterative learning control (P-type ILC) to improve the contour error.
　　The proposed approach can be applied to multi-axis motion systems or robotic systems. The algorithm was implemented on XY-Table and Delta Robot and the results showed that the contour error can be decreased effectively. It is worthwhile to mention that the algorithm can decrease the contour error no matter which coordinates has been used to describe the motion system. From the above, it reveals the feasibility and universality of the proposed method.

摘要 i
ABSTRACT ii
誌謝 iv
目 錄 v
表目錄 viii
圖 目 錄 ix
第一章 緒論 1
1.1 研究目的與動機 1
1.2 文獻回顧 2
1.2.1 輪廓誤差 2
1.2.2 迭代學習控制 3
1.3 研究方法 4
1.4 內容大綱 5
第二章 XY-Table 系統建立 7
2.1 硬體設備介紹 7
2.2 軟體介紹 10
2.3 系統鑑別 11
2.3.1 系統鑑別架構 11
2.3.2 系統鑑別實驗 14
第三章 Delta機器人模型建立 17
3.1 Delta機器人機構簡介 17
3.2 Delta機器人運動學模型 18
3.2.1 Delta機器人之逆向運動學 18
3.2.2 Delta機器人的順向運動學 21
3.2.3 Delta機器人之速度分析 22
3.2.4 Delta機器人之加速度分析 24
3.2.5 Delta機器人之Jerk分析 24
3.3 Delta機器人動力學模型 25
3.3.1 簡介 25
3.3.2 Delta機器人之動力學建模 25
3.4 回授線性化 28
3.4.1 輸入-狀態線性化 28
3.4.2 輸入-輸出線性化 29
3.4.3 機器人線性化 29
第四章 等效輪廓誤差理論 31
4.1 核回歸(Kernel-Based Regression)方法介紹 31
4.2 等效輪廓誤差(Equivalent Contour Error) 35
4.3 曲線規劃與補償 36
4.4 σ的選取 39
4.4.1 實際輪廓誤差估算 41
4.4.2 最佳σ的求取 43
第五章 控制器方法介紹與設計 46
5.1 迭代學習控制 46
5.2 等效輪廓誤差學習控制方法 49
5.2.1 控制方法介紹 49
5.2.2 學習終止條件 54
5.3 控制器架構與設計 56
5.3.1控制器設計流程 56
5.3.2 整體控制架構 58
第六章 XY-Table數值模擬與實驗驗證 60
6.1 運動軌跡速度規劃 60
6.2 數值模擬結果與討論 66
6.2.1 運動軌跡─圓 68
6.2.2 運動軌跡─蝴蝶 78
6.2.3 數值模擬結果討論 81
6.3 實驗結果與討論 83
6.3.1 運動軌跡─圓 84
6.3.2 運動軌跡─蝴蝶 91
6.3.3 實驗結果討論 93
第七章 Delta機器人數值模擬 96
7.1 運動軌跡 96
7.2 數值模擬與討論 97
7.2.1圓軌跡模擬 98
7.2.2 數值模擬結果討論 102
第八章 結論與未來展望 104
參考文獻 105

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