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研究生:林清高
研究生(外文):LIN, CHING-KAO
論文名稱:複合式PID控制器參數最佳化之研究 --以濕式碳酸鈣製程之液位控制為例
論文名稱(外文):A Study of Hybrid of PID Parameter Optimization Process--A Case Study of the Level Control in Wet Calcium Carbonate Production
指導教授:彭世興彭世興引用關係吳德豐
指導教授(外文):PERNG, SHYH-SHINGWU, TER-FENG
口試委員:陳珍源蔡樸生林清富
口試委員(外文):CHEN, JEN-YANGTSAI, PU-SHENGLIN, CHING-FUH
口試日期:2020-07-24
學位類別:碩士
校院名稱:國立宜蘭大學
系所名稱:電機資訊學院碩士在職專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:55
中文關鍵詞:比例-積分-微分控制器液位控制基因演算法灰預測模式
外文關鍵詞:Proportional-Integral-Differential ControllerLevel ControlGenetic AlgorithmGrey Prediction Model
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比例-積分-微分控制器(proportional-integral-derivative, PID)問世迄今已有70年歷史,因其結構簡單、可靠度高、穩定性佳與調整容易,因此成為工業上最廣泛應用的控制器。PID控制器包含三項基本控制參數,即比例、積分、微分參數,透過控制參數調整可控制系統的反應快慢、超越量大小以及震盪程度,使系統可獲得最佳的輸出響應。然而如何設定PID的最佳參數值,除依靠經驗法則外,以往有許多研究也提出各種參數最佳化的演算法,例如基因演算法、模擬退火演算法、粒子群演算法等。然而在實務操作上,已完成設定之PID最佳參數值,常會因為非預期的外部干擾,導致系統產生不穩定狀況。因此,本研究將針對濕式碳酸鈣製程之液位控制個案,提出複合式PID控制器參數最佳化解決方案。系統開始運作時,會先應用基因演算法(GA)進行PID初始最佳參數值搜尋、設定,於系統穩定後再應用適應性灰預測模式(AGM)對系統量測之程序變數進行穩態預測、設定,使系統達到適應性PID最佳化控制之目的。
The Proportional-Integral-Derivative (PID), which appeared over 70 years ago, is the most widely used controller in the industry because of its simple structure, high reliability, good stability, and ease of adjustment and use. A PID controller contains three basic control parameters: proportion, integral, and differential parameters. By adjusting and modifying the response speed of the controllable system, the size of the overstepping, and the degree of system oscillation, the system can achieve the best output response. However, setting the optimal PID parameter value depends on empirical rules, and many previous studies have also proposed various parameter optimization algorithms, such as Genetic Algorithms, Simulate Anneal Arithmetic, and Particle Swarm Optimization. In practice, however, even when the optimal parameter values have been set, systems are often unstable due to unexpected external disturbances. Therefore, this research focused on the wet calcium carbonate process level control and put forward a parameter optimization system for a compound PID controller. The system adopted the genetic algorithm (GA) in the search for system initial optimal parameters of PID value. After the system was stabilized, the adaptive gray model (AGM) of the system was used to predict program variables with the time-period forecast. This ultimately enabled the system to achieve the purpose of controlling the optimization of the adaptive PID.
Furthermore, the cloud host in the background uses a particle filter algorithm to predict battery life. Firstly, the life test data are established in a database, and the life prediction as well as important parts of the battery are carried out using the MATLAB program so that the device can master the service state and life of the whole power circuit and the battery from sensor data, carry out predictive maintenance service, and provide users with suggestions for prolonging battery life. As a result, the consumption of lithium batteries is also reduced and less burden is placed on the environment.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 6
1.3 章節概述 7
1.4 研究流程 8
第二章 PID最佳參數調適文獻探討 9
2.1 PID控制器簡介 9
2.2 PID控制器最佳參數調適法文獻回顧 13
2.3 灰預測理論 17
2.3.1 灰色系統研究內容 17
2.3.2 傳統灰預測模型GM(1,1) 19
2.4 基因演算法 22
第三章 複合式PID最佳參數調適器 26
3.1 GA初始最佳參數解搜尋 27
3.2 適應性灰預測模型 31
3.2.1 趨勢潛力追蹤法 31
3.2.2 適應性灰預測模型建模流程 32
3.3 複合式PID最佳參數調適 34
第四章 實例驗證 36
4.1 生產製程的液位控制迴路 36
4.2 生產製程的流量控制迴路 38
4.3 系統模擬與實驗結果 40
4.3.1 系統啟動初期 40
4.3.2 系統運作期間 45
第五章 結論與未來展望 50
5.1 結論 50
5.2 未來展望 51
參考文獻 52


圖目錄
圖1-1. 碳酸鈣工廠生產線的製程圖 2
圖1-2. 研究個案公司碳酸鈣泥漿生產線的製造控制流程圖 4
圖1-3. 液位計A與流量計A因外部干擾產生不穩定之響應波形圖 4
圖1-4. 流量計A與計算器A因外部干擾產生不穩定之響應波形圖 5
圖1-5. 流量計A與流量計B因外部干擾產生不穩定之響應波形圖 5
圖1-6. 流量計B與研磨機驅動馬達功率因外部干擾產生不穩定之響應波形圖 6
圖1-7. 本研究流程圖 8
圖2-1 比例控制器的控制方塊圖 10
圖2-2. 積分控制器的控制方塊圖 10
圖2-3. 微分控制器的控制方塊圖 11
圖2-4. PID控制器的控制方塊圖 12
圖2-5. 工業中較常使用的PID控制器方塊圖 12
圖2-6. 工業中較常用程變數取代誤差作為微分項的輸入PID控制器方塊圖 13
圖3-1. 複合式PID最佳參數調適器系統方塊圖 26
圖3-2. 基因演算法之演算流程圖 27
圖3-3. 本研究PID編碼方式範例 28
圖3-4. 本研究單點交配方式 29
圖3-5. 本研究單點突變方式 29
圖3-6. 本研究對單一位數存在不可行解時的修補策略範例 30
圖3-7. 本三角TP函數與x(min:i)的TP值 32
圖3-8. 複合式PID最佳參數調適器方塊圖 35
圖4-1. 碳酸鈣生產製程的液位控制迴路製程方塊圖 36
圖4-2. 碳酸鈣生產製程的液位控制迴路控制方塊圖 38
圖4-3. 碳酸鈣生產製程研磨機的流量控制迴路製程方塊圖 39
圖4-4. 碳酸鈣生產製程的研磨機流量控制迴路控制方塊圖 41
圖4-5. 本研究使用MATLAB 2019b繪製之PID模擬系統圖 42
圖4-6. 本研究使用基因演算法進行50次疊代之最佳適合度結果 43
圖4-7. 使用基因演算法搜尋之最佳PID參數值於Simulink之模擬結果(系統穩定) 45
圖4-8. 使用GA搜尋初期最適PID參數值與AGM調整PID參數值之模擬圖 46
圖4-9. 使用GA搜尋初期最適PID參數值與AGM調整PID參數值之模擬圖放大 47
圖4-10. 未使用GA搜尋與AGM調整,僅使用自行PID參數值之模擬圖 47
圖4-11. 未使用GA搜尋與AGM調整,僅使用自行PID參數值之模擬放大 48
圖4-12. 僅使用GA搜尋初期最適PID參數值與AGM調整PID參數值之模擬圖 48
圖4-13. 僅使用GA搜尋最適PID參數值與AGM調整PID參數值之模擬圖放大 49





表目錄
表2-1. Z-N法則的PID參數設定 14
表2-2. 使用最佳化演算法調適PID參數值之文獻彙整 16
表4-1. 本研究進行最佳PID參數值於基因演算法使用參數表 42
表4-2. 本研究使用基因演算法搜尋20次PID最佳參數組合結果 44


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