臺灣博碩士論文加值系統

本論文主要是使用智能計算用於領先-落後控制器的參數設計。在工業中，比例-積分-微分控制器最常被使用，因為其控制器的系統架構簡易且容易設計。但是在參數的設計上需要憑藉過往的經驗來判斷其好壞，且有積分效應與容易受雜訊干擾等等。以往設計領先-落後控制器，通常使用根軌跡或波德圖進行設計，但無法明確的看出與時域空間的關係。於是在本文使用時域空間中的四個指標：尖峰時間、安定時間、過激量百分比、穩態誤差，並結合智能計算中的兩種方法：基因演算法和粒子群聚最佳化於控制器的參數搜尋與設計。首先決定時域空間的四個指標，之後給定指標的最大上限及最小下限的容許範圍，最後運用電腦強大的計算功能，得到近似於範圍內的設計值。

This study primarily uses intelligent computing to determine the parameters of a lead-lag controller. Proportional-integral-derivative controllers are among the most commonly used devices in industries because of their simple system structure and ease of design. However, the quality of the designed parameters must be assessed based on previous experience. In addition, it’s susceptible to noise and integral windup effect. In the past, root locus or bode plots were typically employed when designing lead-lag controller parameters. However, a clear relationship between the parameters and time domains cannot be observed. Therefore, this study employs the four time-domain indices, that is, peak time, settling time, percent overshoot, and steady-state error. Two methods of intelligent computing, genetic algorithm and particle swarm optimization, are combined with the four indices to search and design the controller parameters. The four time-domain indices were determined and allocated a maximum and minimum tolerance. Finally, using powerful intelligent computing capabilities, the design values were obtained within the tolerance.

摘要I
ABSTARCT II
致謝III
目錄IV
圖目錄VII
表目錄IX
第一章 緒論1
1.1 前言1
1.2 文獻回顧2
1.3 研究目的4
1.4 研究架構5
第二章 領先-落後控制系統6
2.1 回授控制系統架構6
2.2 相位領先控制7
2.3 相位落後控制8
2.4 相位領先-落後控制9
第三章 時域指標規格11
3.1 暫態響應11
3.2 穩態誤差13
3.2.1 單位回授系統的穩態誤差 13
3.2.2 非單位回授系統的穩態誤差17
3.3 時域目標函數18
第四章 研究方法20
4.1 初始族群設定20
4.2 基因演算法21
4.2.1 傳統式基因演算法步驟22
4.2.2 改良式基因演算法24
4.3 粒子群聚最佳化25
4.3.1 傳統式粒子群聚最佳化25
4.3.2 改良式粒子群聚最佳化27
4.4 盒鬚圖(Box-plot)27
第五章 實驗結果28
5.1 參數設定說明29
5.2Plant1實驗與模擬結果32
5.3 Plant2實驗與模擬結果37
5.4 Plant3實驗與模擬結果42
5.5 Plant4實驗與模擬結果47
5.6 所有受控體實驗與模擬結果比較52
第六章 結論54
參考文獻55
附錄59
附錄(1.1) GA 的實驗數據-Plant1 59
附錄(1.2) PSO的實驗數據-Plant1 60
附錄(1.3) GA 的實驗數據-Plant2 61
附錄(1.4) PSO的實驗數據-Plant2 62
附錄(1.5) GA 的實驗數據-Plant3 63
附錄(1.6) PSO的實驗數據-Plant3 64
附錄(1.7) GA 的實驗數據-Plant4 65
附錄(1.8) PSO的實驗數據-Plant4 66
附錄(2.1) 粒子群聚最佳化原始程式碼-MATLAB 67
附錄(2.2) 傳統式初始族群設定-Plant1 74
附錄(2.3) 改良式初始族群設定-Plant1 75
附錄(2.4) 基因演算法內部設定參數76
附錄(2.5) 粒子群聚最佳化內部設定參數77
圖目錄
圖2.2 單位回授控制系統方塊圖7
圖2.3 非單位回授控制系統方塊圖7
圖2.4 相位落後控制器的極點-零點分布9
圖3.2單位回授控制系統13
圖3.3非單位回授控制系統17
圖3.4等效控制系統17
圖4.1盒鬚圖示意圖27
圖5.1研究案例的系統架構圖28
圖5.2a GA最佳解的步階響應33
圖5.2b PSO最佳解的步階響應33
圖5.3a Plant1所有較佳解的步階響應34
圖5.3b Plant1所有較佳解的步階響應34
圖5.3c Plant1所有較佳解的步階響應35
圖5.3d Plant1所有較佳解的步階響應35
圖5.4 Plant1所有解的盒鬚圖36
圖5.5a GA最佳解的步階響應38
圖5.5b PSO最佳解的步階響應38
圖5.6a Plant2所有較佳解的步階響應39
圖5.6b Plant2所有較佳解的步階響應39
圖5.6c Plant2所有較佳解的步階響應40
圖5.6d Plant2所有較佳解的步階響應40
圖5.7 Plant2所有解的盒鬚圖41
圖5.8a GA最佳解的步階響應43
圖5.8b PSO最佳解的步階響應43
圖5.9a Plant3所有較佳解的步階響應44
圖5.9b Plant3所有較佳解的步階響應44
圖5.9c Plant3所有較佳解的步階響應45
圖5.9d Plant3所有較佳解的步階響應45
圖5.10 Plant3所有解的盒鬚圖46
圖5.11a GA最佳解的步階響應48
圖5.11b PSO最佳解的步階響應48
圖5.12a Plant4所有較佳解的步階響應49
圖5.12b Plant4所有較佳解的步階響應49
圖5.12c Plant4所有較佳解的步階響應50
圖5.12d Plant4所有較佳解的步階響應50
圖5.13 Plant4所有解的盒鬚圖51 
表目錄
表2.1 相位領先-落後比較表10
表3.1 步階、斜坡、拋物線輸入的穩態誤差分析18
表5.1 四個案例控制器的設計條件31
表5.2 Plant1最佳補償器的參數和步階響應32
表5.3 Plant2最佳補償器的參數和步階響應37
表5.4 Plant3最佳補償器的參數和步階響應42
表5.5 Plant4最佳補償器的參數和步階響應47
表5.6 智能計算實驗結果比較- Plant1 52
表5.7 智能計算實驗結果比較- Plant2 52
表5.8 智能計算實驗結果比較- Plant3 53
表5.9 智能計算實驗結果比較- Plant4 53

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