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研究生: 黃聖凱
論文名稱: 應用模糊類神經法則之球桿平衡控制系統
A Ball-Beam Balance Control System Using Fuzzy-Neural Network Algorithm
指導教授: 曾煥雯
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2005
畢業學年度: 93
語文別: 中文
論文頁數: 64
中文關鍵詞: 模糊推論模糊類神經法則球-桿平衡控制系統
英文關鍵詞: Fuzzy Inference, Fuzzy-Neural Rule, Ball-Beam Balance Control System
論文種類: 學術論文
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  • 摘要
    使用古典控制理論設計控制器,須先找出受控系統的數學模型。因此控制器的性能與受控系統是否能被精確的描述有密切的關係。想要實現智慧型控制,就必須精確的掌握系統特性。「學習」是達到智慧型控制的第一步,經由「學習」可以降低影響動態性能的不確定因素。
    模糊控制理論利用語意資訊,可以將人類知識和經驗轉換成控制法則,具有較佳的強健性和容錯性。類神經網路仿人腦的平行處理方式,具有學習功能,可應用於系統辨識和估測。
    結合模糊推論和類神經網路之模糊類神經控制理論,則同時包含了類神經網路不受模式限定的學習能力,並可根據模糊邏輯,以萃取方式建構知識,具有補足類神經網路“黑盒子”缺點的能力。本研究即是利用模糊類神經法則之推理、學習之特性,並搭配手動操作裝置,將無法清楚描述的控制行為,以模糊推論方式轉換成語意式的模糊規則,並結合類神經網路之學習能力,以期能建立一個使用較少規則和數學,可以吸收人類知識並具有學習功能之球-桿平衡控制系統。

    關鍵字:模糊推論、模糊類神經法則、球-桿平衡控制系統

    Abstract
    It is necessary to find the mathematic model of the plant when we design the controller by classical control theory. Hence, the controller’s control ability is related to the plant which can be described accurately or not. If we want to make up intelligent control, it is necessary to get the system’s characteristics. Learning is the first step to achieve intelligent control. From learning, it can reduce the uncertain factor which can influence the dynamic system.
    Fuzzy control theory uses linguistic information, and it can transform human being’s knowledge and experiments to control rules. It has the better robustness and fault tolerance. Artificial neural network mimics a human brain’s parallel calculation. It has learning capability and it can be applied to system identification and estimate.
    The control theory combining with fuzzy reasoning system and artificial neural network not only have neural network’s learning capability, but also can build knowledge by extracting information form fuzzy logic. Hence it makes up neural network’s drawback which are always treated like a “black box”. This study utilizes the reasoning and learning ability of Fuzzy-Neural rules, and we will use the Fuzzy inference method to transfer the control behavior which can not be expressed clearly to linguistic Fuzzy rule with manual operation device. We will combine they with Neural Networks to establish a ball-beam balance control system which could assimilate human expertise with less rule and mathematics, and learning capability.

    Keyword: Fuzzy Inference, Fuzzy-Neural Rule, Ball-Beam Balance Control System.

    總目錄 中文摘要…………………………………………………………...……….I 英文摘要……………………………………………………………...…...II 總目錄………………………………………………………………….....III 圖目錄………………………………………………………...…………..VI 表目錄…………………………………………………………………….IX 第一章 緒論…………………………………..………………...…………1 1.1 研究背景與動機………………………………….…………...………1 1.2 研究目的………………………………………….……………...……4 1.3 研究範圍與限制………………………………………….………...…4 1.4 研究方法………………………………………………….…………...5 1.5 研究步驟………………………………………………….…………...5 第二章 文獻探討…………………………………………………..……...8 2.1 模糊理論簡介…………………………………………………….…...8 2.2 模糊集合……………………………………...…………………….…9 2.2.1模糊集合之基本性質…………………………..………………10 2.2.2模糊集合之基本運算..…………………………………………12 2.3 模糊推論………………….………………………………………….14 2.3.1模糊推論方式…………………………………………..………15 2.4 模糊控制………………….………………………………………….16 2.5 類神經網路簡介……………………………………………….…….21 2.6 位置判斷-影像……………………………………………………….24 2.6.1 影像二值化……………………………………………………24 2.6.2 移動物體偵測…………………………………………………25 2.6.3 影像型態學……………………………………………………29 第三章 系統架構設計……………………………………………..…….31 3.1 系統建構………………………………………………………….….31 3.1.1 硬體設備………………………………………………………31 3.1.2 系統配備說明…………………………………………………33 3.1.3 系統軟體………………………………………………………35 3.2 系統設計………………………………………………………….….36 3.2.1系統說明……………….……………………………………….25 3.2.2 模糊類神經網路..………………………………………….….27 3.3 控制器設計……………………………………………………….….28第四章 實驗步驟與結果…………………………………………..…….41 4.1 系統測試………………………………………………………….….41 4.2 測試設計………………………………………………………….….44 4.2.1 操控者控制結果…………………………………………...….44 4.2.2 建立模糊規則…………………………………………………50 4.3 模擬結果………………………………………………………….….54 4.4 實際運轉情形…………………………………………………….….56 第五章 結論與討論……………………………………………………...59 5.1 結論…………………………………………………………………..60 5.2 討論…………………………………………………………………..60 參考文獻………………………………………………………………….62 圖目錄 圖 1-1 研究步驟流程圖……………………………………………...…..7 圖 2-1 模糊集合基本性質之示意圖…………………………………...11 圖 2-2 車速的歸屬函數圖……………………………………………...11 圖 2-3 直接推論法的演算過程………………………………………...15 圖 2-4 模糊邏輯控制器方塊圖………………………………………...16 圖 2-5 Delta Learning Rule示意圖………………………………….….22 圖 2-6 二值化影像……………………..……………………………….24 圖 2-7 差值影像(一)…………………………………………………….26 圖 2-8 差值影像(二)…………………………………………………….26 圖 2-9 移動物體檢測流程………………………………………………27 圖 2-10 移動物體檢測……………………………………………….….27 圖 2-11 3X3影像………………………………………………………...28 圖 2-12 Sobel邊緣影像………………………………………………….28 圖 3-1 CCD………………………………………………………………31 圖 3-2 影像擷取卡……………….………………………………….….31 圖 3-3 AD/DA卡…………………………………………………….….32 圖 3-4 搖桿……………………………………………………………...32 圖 3-5 馬達驅動器……………………………………………………...32 圖 3-6 直流馬達………………………………………………………...32 圖 3-7 實體架構……………………….…………………………….….33 圖3-8 滑台-桿平衡系統機構正視圖…………………………………..33 圖 3-9 系統架構示意圖…………………………………………………36 圖 3-10 滑台-桿平衡控制架構…………………………………………36 圖 3-11 模糊類神經架構……………………………………...………...38 圖 3-12 神經元示意圖…………………………………………...……...38 圖 4-1 金屬滑台…………………………………………………………41 圖4-2 搖桿位置訊號與電壓轉換關係圖…………………………...…..42 圖 4-3 搖桿控制訊號(一)……………………………………………….44 圖 4-4 電位計輸出電壓(一)…………………………………………….45 圖 4-5 目標物收斂情形(一)…………………………………………….45 圖 4-6搖桿控制訊號(二)………………………………………………..46 圖 4-7 電位計輸出電壓(二)…………………………………………….46 圖 4-8 目標物收斂情形(二)…………………………………………….47 圖 4-9搖桿控制訊號(三)………………………………………………..47 圖 4-10 電位計輸出電壓(三)…………………………………………...48 圖 4-11目標物收斂情形(三)…………………………………………….48 圖 4-12搖桿控制訊號(四)………………………..……………………..49 圖 4-13 電位計輸出電壓(四)……………………………………...……49 圖 4-14 目標物收斂情形(四)…………………………………………...50 圖 4-15 系統操控示意圖…………………………………………….….50 圖 4-16 輸入參數與輸出結果之歸屬函數...…….……………………52 圖 4-17 系統模糊規則...………………………………………………...54 圖 4-18 電位計輸出電壓……………………...………………………...54 圖 4-19 目標物收斂情形…………………………………………….….55 圖 4-20 電位計輸出電壓…………………………………………….….55 圖 4-21 目標物收斂情形…………………………………………….….56 圖 4-22 模糊類神經學習結果(一)……………………………..……….56 圖 4-23 模糊類神經學習結果(二)……………………………………...57 圖 4-24 系統運轉測試(由左到中)…………………….………………..58 圖 4-25系統運轉測試(由右到中)…..…………………..………………59 表目錄 表 2-1 常用之三角範數(norm)….……………………………………12 表 2-2 常用之反三角範數(norm).……………………………………13 表 2-3 廣義的肯定前件論式…………………………………………14 表 2-4 廣義的否定後件論式…………………………………………14 表 4-1 12 bits解析度之DA電壓與數值轉換表………………………42 表 4-2 12 bits解析度之AD電壓與數值轉換表……………………....43 表 4-3 電位計電壓輸出值與角度對應關係……………………….…44

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