利用遞迴小波模糊類神經網路於永磁輔助同步磁阻馬達位置驅動系統之智慧型步階回歸控制

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簡佑宸(Yu-Chen Chien) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用遞迴小波模糊類神經網路於永磁輔助同步磁阻馬達位置驅動系統之智慧型步階回歸控制
(Intelligent Backstepping Control of Permanent Magnet Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本研究的目標是開發一種用於永磁輔助同步磁阻電機(PMASynRM)的智慧型伺服驅動系統，該系統能夠適應電機的非線性和時變控制要求。為此，提出了一種具有智慧型步階回歸控制的遞迴小波模糊神經網路(RWFNN)。首先，引入ANSYS Maxwell-2D動態模型來控制PMASynRM驅動器的最大每安培扭矩(MTPA)。根據有限元分析(FEA)結果創建查找表(LUT)，以確定 MTPA內的當前指令角。接下來，開發了基於步階回歸(BSC)的系統來跟踪位置命令。然而，由於事先無法獲得不確定性訊息，為PMASynRM位置伺服驅動系統的實際應用設計有效的BSC具有挑戰性。因此，本研究提出了一種 RWFNN 來近似 BSC，並減輕了與使用傳統 BSC 相關的上述困難。改進的自適應補償器被添加到 RWFNN 以解決潛在的近似誤差。為保證RWFNN的穩定性，採用Lyapunov穩定性方法生成RWFNN的在線學習算法並保證漸近穩定性。實驗結果證明了所提出的 IBSCRWFNN 控制的 PMASynRM 驅動器的有效性和強健性。最後，本研究採用32位元浮點運算數位訊號處理器TMS320F28075，以實現所提出的智慧型控制於永磁輔助同步磁阻馬達驅動系統中。

摘要(英)

The goal of this study is to develop an intelligent servo drive system for a permanent magnet assisted synchronous reluctance motor (PMASynRM) that can adapt to the motor′s nonlinear and time-varying control requirements. To achieve this, a recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed. First, an ANSYS Maxwell-2D dy-namic model is introduced to control the maximum torque per ampere (MTPA) of the PMASynRM servo drive. A lookup table (LUT) is created based on finite element analysis (FEA) results to determine the current angle of command within the MTPA. Next, a system based on backstepping control (BSC) is developed to track the position reference. However, designing an effective BSC for practical applications of the PMASynRM position servo drive system is chal-lenging due to the unavailability of uncertainty information in advance. Therefore, this study proposes an RWFNN to approximate the BSC and alleviate the above difficulties associated with using a traditional BSC. An improved adaptive compensator is added to the RWFNN to address potential approximation errors. To ensure the stability of the RWFNN, the Lyapunov stability method is used to generate the RWFNN′s online learning algorithms and guarantee asymptotic stability. Experimental results demonstrate the effectiveness and robustness of the proposed in-telligent backstepping control recurrent wavelet fuzzy neural network (IBSCRWFNN) controlled PMASynRM servo drive.
Finally, the intelligent control system and the vector mechanism for the PMASynRM drive are successfully implemented utilizing the TMS320F28075, a 32-bit floating point digital signal processor (DSP)

關鍵字(中)

★ 永磁輔助同步磁阻馬達
★ 每安培最大轉矩
★ 有限元素分析
★ 步階回歸控制
★ 遞迴小波模糊類神經網路

關鍵字(英)

★ permanent magnet assisted synchronous reluctance motor (PMASynRM)
★ maximum torque per ampere (MTPA)
★ finite element analysis (FEA)
★ backstepping control (BSC)
★ intelligent backstepping control recurrent wavelet fuzzy neural network (IBSCRWFNN)

論文目次

摘要 I
Abstract II
目錄 IV
圖目錄 VII
表目錄 XIV
第一章緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 3
1.3 論文貢獻 6
1.4 論文大綱 7
第二章永磁輔助同步磁阻馬達之控制板和驅動系統之控制板介紹 8
2.1 前言 8
2.2 TMS320F28075數位訊號處理器簡介 10
2.3 TMS320F28075數位訊號處理器控制板之電路 11
2.3.1 電壓源轉換電路 12
2.3.2 數位/類比轉換電壓準位轉換電路 13
2.4 輸入/輸出板之電路 13
2.4.1 ADC輸入腳位之過電流保護電路 14
2.4.2 過電流保護電路 15
2.4.3 數位/類比轉換電路 17
2.5 外部負載控制電路 17
2.6 功率級系統建構 20
2.6.1 前言 20
2.6.2 電容板設計 20
2.6.3 主功率級設計 21
2.6.4 閘級驅動設計 22
2.6.5 米勒箝位驅動原理 23
第三章永磁輔助同步磁阻馬達驅動系統 26
3.1 前言 26
3.2 永磁輔助同步磁阻馬達 28
3.3 永磁輔助同步磁阻馬達數學動態模型與三相座標轉換 29
3.3.1 永磁輔助同步磁阻馬達在abc座標系下之數學模型 32
3.3.2 永磁輔助同步磁阻馬達在座標系下之數學模型 34
3.3.3 永磁輔助同步磁阻馬達在dq座標系下之數學模型 37
3.3.4 永磁輔助同步磁阻馬達反電動勢定義 40
3.4 永磁輔助同步磁阻馬達對位方法 42
3.5 增量型編碼器位置迴授和速度估算 44
3.6 永磁輔助同步磁阻馬達控制架構 44
3.6.1 有限元素分析每安培最大轉矩控制 44
3.6.2 比例積分、步階回歸和利用遞迴小波模糊類神經網路之智慧型步階遞迴控制 45
3.6.3 電流比例積分控制器之設計 47
3.6.4 速度比例積分控制器之設計 49
3.6.5 位置比例控制器之設計 51
第四章永磁輔助同步磁阻馬達步階回歸控制 53
4.1 前言 53
4.2 步階回歸控制原理 53
4.3 步階回歸控制穩定性證明 55
第五章永磁輔助同步磁阻馬達利用遞迴小波模糊類神經網路之智慧型步階回歸控制 57
5.1 前言 57
5.2 利用遞迴小波模糊類神經網路之智慧型步階回歸控制系統 57
5.3 遞迴小波模糊類神經網路之架構 58
5.4 遞迴小波模糊類神經網路穩定性證明 62
第六章永磁輔助同步磁阻馬達之有限元素分析 69
6.1 前言 69
6.2 建立永磁輔助同步磁阻馬達有限元素分析模型 70
6.2.1 Maxwell 2D 模型建立永磁輔助同步磁阻馬達 71
6.2.2 ANSYS功能設定 72
6.3 Simplorer 模擬分析 75
6.3.1 ECE簡介 75
6.3.2 ECE建模設定 76
6.3.3 比例積分控制、步階回歸控制與利用遞迴小波模糊類神經網路之智慧型步階回歸控制模擬結果 78
第七章實驗結果與討論 85
7.1 前言 85
7.2 實驗結果 86
7.3 實驗結果之討論 96
第八章結論與未來展望 99
8.1 結論 99
8.2 未來展望 99
參考文獻 100
作者簡歷 108

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2023-8-3

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