臺灣博碩士論文加值系統

本論文主要是基於適應性智慧型控制方法進行三維天車控制，其設計目標為解決傳統控制設計需求得系統動態模型之問題，為了解決此問題將應用模糊邏輯系統進行控制器設計。使用模糊邏輯系統之優點為可提供一個具有效率之系統架構，用以處理人們對問題描述時所包含的語意模糊信息。同時在控制器設計中結合適應性控制的概念，目的在於處理系統參數與架構不確定之情況下維持一定的控制效能。其中所使用的學習演算法為倒傳遞演算法，然而一般使用此演算法需要額外設計鑑別器以求取系統靈敏度，用於提供模糊邏輯控制器參數調整使用。為了因應系統靈敏度不易求得之問題，將應用近似系統靈敏度方向的方法，達到免鑑別器設計與簡化運算的優點。並且在設定追蹤目標時應用軌跡規劃概念，使得移動安全性更加提升。最後使用實驗室三維天車系統進行即時控制並且更換不同重量之負載進行實驗，以驗證所設計之適應性模糊邏輯控制器之效能與可行性。

This thesis is mainly based on adaptive intelligent control method for three-dimensional overhead crane system. The design objective is to resolve issues related to the need for system dynamic models in the design of conventional controllers; for this purpose, a fuzzy logic system was used in the controller’s design. The advantage of fuzzy logic systems is that they provide an effective system architecture, which can be used to process the semantic fuzzy information contained in the human descriptions of a problem. The concept of adaptive control has also been integrated within the controller’s design. The purpose of this feature is to maintain a level of control even when the processing system’s parameters and architecture are in a state of uncertainty. The back propagation algorithm was used as the controller’s learning algorithm; however, the use of this algorithm usually requires the use of an external system identifier to obtain the system’s sensitivity in order to provide the fuzzy logic controller with parameters for adjustment and calibration. As the sensitivity of a system cannot be obtained easily, we used a method by which the system’s sensitivity and orientation were approximated. This sidesteps the need for system identifiers and simplifies the required calculations. And the concept of motion planning is applied when setting the tracking target, so that the safety of movement is further improved. Finally, real-time control experiments were performed on a laboratory three-dimensional overhead crane system, using loads of varying weight, in order to validate the viability and effectiveness of the adaptive fuzzy logic controller designed in this study.

第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 3
1.3 論文架構 5
第二章 三維天車系統動態模型與機構分析 6
2.1 機構規格整理 6
2.2 三維天車系統動態模型 7
2.2.1座標系統 7
2.2.2 非線性動態模型 8
2.2.3 線性動態模型 10
2.3 脈波單位與電子齒輪比設定 12
2.3.1 PLS位置單位 12
2.3.2 PUU單位與電子齒輪比 13
2.3.3電子齒輪比公式推導 16
2.4 三維天車機構設定 18
2.4.1 攝影機角度感測系統 18
2.4.2 攝影機角度感測系統編碼解碼設定 20
2.4.3 微處理器角度感測系統建立 23
2.4.4 微處理機角度感測系統編碼解碼設定 28
2.5 Z軸PUU脈波編碼回授訊號轉換為負載實際距離 29
2.5.1距離感測器建立 31
2.5.2 長度編碼解碼設定 34
2.5.3 實驗設計與驗證 35
第三章 模糊邏輯與模糊類神經網路 41
3.1模糊邏輯應用於貨櫃吊運控制 41
3.1.1實施語言控制策略 43
3.1.2定義語言變數 44
3.1.3模糊推論 48
3.1.4解模糊化 50
3.2 模糊類神經網路 52
第四章 適應性智慧型控制器設計 57
4.1 基礎定位應用比例控制器 57
4.1.1 增益值設定 57
4.1.2 實驗結果 58
4.2 抗擺盪模糊邏輯控制器設計 62
4.2.1設定輸入變數與歸屬函數 62
4.2.2 設計抑制晃動之規則 65
4.2.3 解模糊化 67
4.2.4 實驗結果 68
4.3 模糊邏輯控制器應用於定位與抗擺盪 72
4.3.1 設定輸入變數與歸屬函數 72
4.3.2 設計定位與抑制晃動之規則 75
4.3.3 解模糊化 77
4.3.4 實驗結果 77
4.4 適應性模糊邏輯控制器用於定位與抗擺盪 82
4.4.1輸出單值歸屬函數與規則對應關係之建立 82
4.4.2 定義目標函數 84
4.4.3 應用倒傳遞演算法推導適應律 84
4.4.4 實驗結果 89
4.4.5 結果評估與比較 97
4.4.6 結論與貢獻 98
第五章 三軸定位控制與抗擺盪之實現 101
5.1 雙適應性模糊邏輯控制 101
5.1.1 適應性模糊邏輯控制器應用於Z軸定位控制 101
5.1.2 設定輸入變數與歸屬函數用於Z軸定位控制 102
5.1.3 設計Z軸定位控制規則 104
5.1.4 Z軸定位控制解模糊化 106
5.1.5 應用倒傳遞演算法調整Z軸定位控制器參數 106
5.2 雙適應性模糊邏輯控制器應用於X軸 110
5.2.1 設定輸入變數與歸屬函數用於X軸定位控制 111
5.2.2 應用倒傳遞演算法調整X軸定位控制器參數 113
5.2.3 設計輸入變數與歸屬函數用於X軸抗擺盪控制 116
5.2.4 設計X軸抗擺盪控制規則 118
5.2.5 X軸抗擺盪控制解模糊化 119
5.2.6 應用倒傳遞演算法調整X軸抗擺盪控制器參數 119
5.2.7 實驗結果 123
5.3 雙適應性模糊邏輯控制器結合軌跡規劃 133
5.3.1 XY軸軌跡規劃 133
5.3.2 設定輸入變數與歸屬函數用於X軸定位控制 134
5.3.3 實驗結果 136
第六章 結論與未來展望 153
6.1結論 153
6.2未來展望 153
參考文獻 155

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