臺灣博碩士論文加值系統

由於近年來自動化的趨勢越來越受到關注，使得精密平台也開始被廣泛的應用，而如何使精密平台能有好的控制及軌跡追隨，則必須要有好的控制方法來控制平台，所以控制方法的研究非常值得我們去探討的。在本論文中，提出了雙軸壓電平台系統的高精度軌跡以及追蹤控制的直接自適應模糊小腦模型滑膜控制器，將傳統的小腦模型加入高斯函數作為基底函數成為模糊小腦模型。自適應分為直接和間接兩種方式，直接小腦模型控制的理論可以對於參數的不確定性和外界負載干擾進行推導並分析這兩個因素對於系統的穩定性分析。我們利用Lyapunov穩定性定理用來證明對於整個系統的漸進穩定性和所有不確定的參數在閉迴路的系統。在模擬的情況，可以依照提出的非線性壓電馬達數學方法和方法來做模擬壓電平台的軌跡測試，經過模擬再以實際平台做測試，我們依照提出的方法以壓電平台做軌跡的運行。在模擬和實作的情形所使用系統皆為MATALB的方式，使用 MATLAB的Simulink Embedded function的方式。所做的軌跡測試則是Non-Uniform Rational B-Spline(NURBS)曲線方法設計圖形，有設計的四種圖形分別為圓形、蝴蝶結、心形和星形，以這四種圖形進行模擬以及實測的軌跡追蹤。在結果來看，模擬以及壓電平台實驗都呈現不錯的軌跡追蹤，最後透過計算追隨平均誤差和追隨誤差之標準差，實驗結果顯示所提出的方法均優於比較方法，模糊小腦模型能夠有效的降低誤差並有更好的軌跡追隨呈現。

As the trend of automation has been paid more and more attention in recent years, the precision platform has begun to be widely used. Nevertheless, it is important to make the precision platform have good control and trajectory tracking. It is necessary to have a good control method to control the platform, so the study of control methods is very worthy of our discussion. In this thesis, adaptive fuzzy cerebellar model articulation controller (FCMAC) sliding mode controller is designed and proposed for high-precision trajectory control of X-Y axis piezoelectric motion system. Adding the traditional cerebellar model to the Gaussian function as the basis function becomes the fuzzy cerebellar model. The proposed FCMAC control structures can be used to compensate the parameter uncertainty and external load disturbance. The Lyapunov stability theorem is used to prove the asymptotic stability of the entire system and all uncertain parameters are in closed loop systems. Trajectory planning is based on Non-Uniform Rational B-Spline (NURBS) curve design graphics. The X-Y piezoelectric motion system is experimentally and simulation investigated with four typical contours, namely, circle, bowknot, heart and star reference contours. The experimental results show that the proposed method is superior to the comparison method, and the fuzzy cerebellar model can effectively reduce the error and have better trajectory tracking.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 文獻探討 2
1.4 研究方法 4
1.5 論文內容架構 5
第二章 運動控制平台系統架構 6
2.1系統架構 6
2.2運動控制卡 6
2.2.1 運動控制卡 PCI-1240U 6
2.2.2數位轉類比卡 PCI-1716 7
2.2.3專用端子版ADAM-3952 8
2.3光學尺 9
2.3.1 LIA線性系列光學尺 9
2.3.2光學尺帶 9
2.3.3光學尺原理 10
2.3.4編碼器 10
2.4馬達驅動器 11
2.5馬達 12
2.6電源供應器 14
2.7壓電平台數學模型 15
2.8數學模擬工具Matlab 17
2.8.1 MATLAB環境介紹 17
2.8.2 Real-Time-Workshop(RTW)工具箱 17
2.8.3 S-function 18
第三章 控制方法 19
3.1 CMAC小腦模型 19
3.1.1 CMAC基本架構 19
3.1.2 一維CMAC 20
3.1.3 二維CMAC 23
3.1.4 模糊CMAC 25
3.2 自適應控制 27
3.3 直接自適應模糊小腦模型滑模方法 27
3.3.1 系統描述 27
3.3.2直接自適應滑模控制器設計 28
3.4 NURBS曲線軌跡規劃 31
3.4.1NURBS基本介紹 31
3.4.2 圓形軌跡規劃 33
3.4.3 蝴蝶結軌跡規劃 34
3.4.4 心形軌跡規劃 35
3.4.5 星形軌跡規劃 36
第四章 實驗結果 37
4.1 實驗方式 37
4.2 模擬結果 42
4.2.1 模擬的參數 42
4.2.2 模擬的軌跡 43
4.2.3 模擬的數據統計 53
4.3 實驗結果 56
4.3.1 實驗的參數 56
4.3.2 實驗的軌跡 56
4.3.3 實驗的數據統計 67
第五章 結論與未來展望 69
5.1 結論 69
5.2 未來展望 69
參考文獻 70

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