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研究生:官義雄
研究生(外文):Yi-Hsiung Kuan
論文名稱:模糊PID控制器之設計與模擬
論文名稱(外文):Design and Simulation of Fuzzy PID Controller
指導教授:洪 麟
指導教授(外文):Lin Hong
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電機工程系碩士班
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:2007
畢業學年度:95
語文別:中文
論文頁數:61
中文關鍵詞:模糊PID控制器試誤法規則庫比例因子基因演算法
外文關鍵詞:Fuzzy PID controllerTrial and error methodRule baseScaling factorGenetic algorithm
相關次數:
  • 被引用被引用:2
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眾所皆知的傳統PID控制器一般不能對非線性系統、高階系統和延遲線性系統和特殊複雜或含不確定性系統有較好的控制性能。為了解決這些問題,必須有較好的控制器設計方法,就是把人類之智慧與經驗轉變成自動控制法則,而這就是頗受重視之模糊控制。通常模糊PID控制器之輸入為誤差和誤差變率,而許多模糊PID控制器可以利用兩個模糊控制器結合(PI+PD、PI+D、PD+I)為一個模糊PID控制器;或是將模糊控制器以疊代方式形成階層式模糊PID控制器,這是為了避免輸入維數過高。
因此,這樣的控制器須要有太多的模糊規則庫。為了解決這問題,本論文嘗試把傳統兩個模糊控制器或規則庫較多之複雜控制器經由分析合併,改以簡單的單一規則庫(9條)來取代。從模擬結果來看,作者所提供的方法無論對線性系統和非線性系統,在不須要插入太複雜和增加維數的規則庫情況下,就可以得到良好操作性能。
其次,傳統模糊PID控制器的輸出入比例因子都必須靠人員累積的經驗或是利用試誤法(trial and error)不斷的調整,沒有一定的規則可尋。所以作者提出利用基因演算法(GA)來調整模糊PID控制器的比例因子。與傳統試誤法比較起來,這個方法可以節省大量的時間跟精力,也不需要額外的有關受控系統的專業知識及經驗。以這些例子經由電腦模擬的結果與數據證明,在性能上可以達到滿意的控制結果,顯現出我們所提出之方法的優點。
The best known conventional PID controllers generally do not work well for nonlinear systems, higher order or time delayed linear systems, particularly for those complex or uncertain systems which have no precise mathematical models. An alternative that can be used to solve these problems and get a better controller performance is to incorporate human intelligence into automatic control algorithm by developing fuzzy logic controller. Most of the PID type FLC applications in the literatures are designed using error and change of error as input variables. Mean while, to avoid the rapid dimension increasing of input variables, researchers usually use two cascaded(PI+PD、PI+D、PD+I) fuzzy controllers to form a fuzzy PID controller. The hierarchical FLC which uses the first fuzzy control output as the second fuzzy controller's input is also adopted.
However, the above mentioned controllers usually have too many fuzzy rule bases. In order to solve this problem, the author did a thoroughly analysis of existing papers, combined those complex rule bases, as well as made extensive modification of membership functions. Finally, we got the results of simplest fuzzy controller with only nine rules. The simulation shows that the new methodology improves the performance of both linear and nonlinear systems.
On the other hand, the scaling factors of conventional fuzzy PID controllers must be adjusted according to personal experiences or to depend on trial and error design method. It does not have any certain rule to follow up. We propose the fuzzy PID controller with the scaling factors turned by using genetic algorithm (GA) method. This technique can save much time and effort than that of conventional trial and error method. It does not need extra professional knowledge or mathematical analysis of the system dynamics. Some illustrative examples demonstrate that satisfactory performance is achieved.
摘 要 i
Abstract ii
圖 目 錄 v
表 目 錄 vii
第一章 緒論 1
1-1 研究動機和目的 1
1-2 文獻回顧 2
1-3 內容大綱 3
第二章 基因演算法 4
2-1 簡介 4
2-2 基因演算法基本理論 4
2-2.1 適應函數(Fitness Function) 5
2-2.2 再生(Reproduction) 5
2-2.3 交配(Crossover) 6
2-2.4 突變(Mutation) 7
2-2.5 基因演算法的應用步驟[6] 7
2-2.6 基因演算法流程 8
第三章 模糊控制理論 9
3-1簡介 9
3-2模糊控制器基本原理 9
3-2.1模糊化介面 10
3-2.2模糊知識庫 12
3-2.3模糊推論引擎 12
3-2.4解模糊化介面 13
第四章 模糊控制器之應用 14
4-1模糊PID控制器之架構 14
4-2新的PI+D型階層式模糊控制器 17
4-3結合模糊PI和PD控制器之強健自我調整架構 25
4-4系統討論 41
第五章 模糊控制器之基因演算法應用 42
5-1 誤差準則(Error Criteria) 42
5-2 基因演算法模糊PID控制之步驟 43
5-3 基因演算法模糊PID之比例因子調變 44
5-3.1 Zhi-Wei Woo[ 21]之自我調整比例因子 44
5-3.2 Escamilla-Ambrosio[22]之參數對照法 49
5-3.3系統討論 57
第六章 結 論 58
參 考 文 獻 59
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