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博碩士論文 etd-0801116-205956 詳細資訊
Title page for etd-0801116-205956
論文名稱
Title
多目標粒子群最佳化控制器於永磁同步馬達之設計與應用
Design and Implementation of a MOPSO Controller for PMSM
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
84
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-07-27
繳交日期
Date of Submission
2016-09-06
關鍵字
Keywords
智慧型演算法、模糊控制、多目標粒子群演算法、數位訊號處理器、永磁同步馬達
Intelligent algorithm, PMSM, MOPSO, Embedded system, Fuzzy
統計
Statistics
本論文已被瀏覽 5671 次,被下載 18
The thesis/dissertation has been browsed 5671 times, has been downloaded 18 times.
中文摘要
由於現代科技進步發展快速,各式各樣的智慧型演算法已大量使用於日常生活中。有較普遍的模糊邏輯、類神經演算法,以及各式機器學習方法等等。大量的智慧型演算法不斷改善各式系統問題,同時在改良真實系統問題時也發現各種演算法的不足與限制,故不斷地透過數學方法進行演算法改良。
本論文將應用最新提出之分解與支配之多目標粒子群演算法於尋找及改善永磁同步馬達控制器參數,提供使用者可在不同最佳化目標間選擇的控制器設計,例如擁有更好的節能效果或是更快的系統響應,並與不同演算法比較,分別為基於非支配排序基因多目標最佳化演算法以及多目標粒子群演算法與擁擠距離。基因演算法模仿生物學中生物遺傳演化逐漸產生更優秀子代的過程來求解最佳化問題,而粒子群演算法則描述鳥類覓食尋找最佳位置求解最佳化問題。
本論文首先將以MATLAB實現各項最佳化演算法,並透過模擬尋找最佳化模糊控制器參數。再透過模糊集結合權重的概念在最佳前緣上找出最後的控制器參數寫入數位訊號處理器(DSP28069),最後設計實驗與東元變頻器通訊讀取永磁同步馬達各項參數以及驗證最終設計結果,並證明演算法於真實系統上擁有符合預期之控制器改善設計結果,亦可改善永磁同步馬達在負載變化之各種目標需求。
Abstract
Lots of intelligent algorithms have been applied to our daily life such as fuzzy theory, neural network, machine learning and so on. These methods are widely used in solving many kinds of real-world problems while deficiencies and limitation of those algorithms are also found.
This paper will introduce the recently improved algorithm which called multiobjective particle swarm optimization based on decomposition and dominance (D2MOPSO) to design the fuzzy controller of PMSM in different objects. That means the user could change customized controller according to their requirement easily. This paper also compares the final decision of controller parameter with two other algorithms: multiobjective particle swarm optimization with crowding distance (MOPSO-CD) and nondominated sorting genetic algorithm II (NSGA-II).
Simulation results of three algorithms will show the best controller parameter of PMSM in computing software MATLAB. Finally, we implement the fuzzy controller on embedded system (DSP28069) to prove our design is just matching the reality system response and is easier to meet the user’s demand.
目次 Table of Contents
論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
目 錄 v
圖 次 viii
表 次 xi
第一章 緒 論 1
1-1 前言 1
1-2 研究背景 2
1-3 研究目的 6
1-4 章節簡介 8
第二章 永磁同步馬達之模擬 9
2-1 前言 9
2-2 模糊控制 10
2-2-1 定義變數及模糊化 10
2-2-2模糊推論 11
2-2-3解模糊化 12
2-3 馬達轉速模擬 13
2-3-1數學模型推導 13
2-3-2高斯雜訊 16
2-3-3轉速響應模擬圖 17
第三章 多目標最佳化演算法 18
3-1 前言 18
3-2 多目標粒子群演算法及擁擠距離(MOPSO-CD) 19
3-2-1 多目標最佳化問題 19
3-2-2 粒子群演算法 19
3-2-3 擁擠距離 20
3-2-4 突變法則 21
3-2-5 MOPSO-CD演算法流程 22
3-3 分解與支配之多目標粒子群演算法(D2MOPSO) 24
3-3-1 二維擁擠距離於不同空間 24
3-3-2 分解與支配方法 24
3-3-3 D2MOPSO演算法流程 28
3-4 多目標粒子群測試 30
3-4-1測試函數結果 30
3-5 非支配排序基因多目標最佳化演算法(NSGA-II) 37
3-5-1快速非支配解排序方法 37
3-5-2多樣性的維持 38
3-5-3 NSGA-II演算法流程 39
3-5-4測試函數結果 40
第四章 實驗控制平台 41
4-1 前言 41
4-2 東元A510變頻器(TECO INVERTER A510 Series) 41
4-3 東元永磁同步馬達 42
4-4 動力計平台 44
4-5 數位訊號處理器 45
4-6 開發軟體 46
4-6-1 Code Composer Studio 46
4-6-2 RAD Studio C++ Builder XE2 47
第五章 實驗結果 48
5-1 前言 48
5-2 多目標最佳化演算法模擬結果 49
5-3 最終決策與模擬結果 54
5-4 實驗結果 63
第六章 結論與未來展望 66
6-1 結論 66
6-2 未來展望 67
參考文獻 68
參考文獻 References
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