利用多項式派翠類神經網路於高效能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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孫子翔(Tzu-Shiang Sun) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用多項式派翠類神經網路於高效能同步磁阻馬達位置驅動系統之智慧型步階回歸控制
(Development of High-Performance Synchronous Reluctance Motor Position Servo Drive Using Intelligent Backstepping Control)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本研究目的在創建一個優化的同步磁阻馬達伺服驅動系統，通過使用具有智慧型步階回歸控制功能的多項式派翠模糊類神經網路來改變同步磁阻馬達固有的非線性和時變控制特性本研究首先介紹了一個 ANSYS Maxwell-2D 動態模型，其中包含每安培最大扭矩控制的同步磁阻馬達伺服驅動器。建構查表法以組成有限元分析的結果，以生成每安培最大轉矩控制的直軸電流命令。隨後，本研究設計了一個步階回歸控制系統來跟踪位置參考命令。在實際應用上步階回歸控制的設計非常複雜，因為事先無法獲得詳細的系統動態參數，包括同步磁阻馬達伺服驅動系統的不確定項。因此這項研究明，多項式派翠模糊類神經網路可以作為理想步階回歸控制的近似優化來解決其現有的問題。此外，本研究修改了自適應補償器以主動調整多項式派翠模糊神經網路的潛在計算偏差。使用生成多項式派翠模糊類神經網路在線學習算法的李亞普諾夫穩定性方法可確保系統的漸近穩定性。最後，實驗提供了一些結果來驗證所提出的智慧型步階回歸多項式派翠模糊類神經網路控制的同步磁阻馬達伺服驅動器其有效性和強健性。

摘要(英)

This study aims to create an optimized synchronous reluctance motor (SynRM) servo drive system to alter the inherent nonlinear and time-varying control characteristics of the SynRM by using an intelligent backstepping control polynomial Petri fuzzy neural network (IBSCPPFNN) that features an intelligent backstepping control. This study first introduces an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) controlled SynRM servo drive. A lookup table (LUT) is built to compose of the finite element analysis (FEA) results to generate direct axis current command of the MTPA. Subsequently, this study designs a backstepping control (BSC) system to track the position reference command. Creating a working BSC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the SynRM servo drive system, is not available beforehand. Thus, this study suggests that a polynomial Petri fuzzy neural network (PPFNN) can act as a close substitute for the ideal BSC to resolve its existing complications. Furthermore, this study modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the PPFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the PPFNN’s online learning algorithms. Finally, some experimental results are provided to verify the effective and robust qualities of the suggested IBSCPPFNN controlled SynRM servo drive.

關鍵字(中)

★ 同步磁阻馬達
★ 每安培最大轉矩控制
★ 有限元素分析法
★ 步階回歸控制
★ 智慧型步階回歸多項式派翠模糊類神經網路控制

關鍵字(英)

★ Synchronous Reluctance motor (SynRM)
★ Maximum torque per ampere (MTPA)
★ Finite element analysis (FEA)
★ Backstepping control (BSC)
★ Polynomial Petri fuzzy neural network (PPFNN)

論文目次

摘要. I
Abstract VI
目錄 …………………………………………………………………..VIII
圖目錄 XI
表目錄 XVII
第一章緒論 18
1.1 研究動機與目的 18
1.2 文獻回顧 24
1.3 論文貢獻 27
1.4 論文大綱 28
第二章同步磁阻馬達驅動系統之控制板介紹 29
2.1 前言 29
2.2 TMS320F28075數位訊號處理器簡介 32
2.3 以DSP為基礎的同步磁阻馬達控制驅動系統 34
2.4 TMS320F28075 DSP 控制板與其電路 35
2.5 輸入/輸出板 36
2.6 外部負載控制電路 37
第三章同步磁阻馬達驅動系統 40
3.1 前言 40
3.2 同步磁阻馬達 42
3.3 座標轉換之電壓及磁阻轉矩方程式 45
3.4 同步磁阻馬達其對位方式 50
3.5 建立同步磁阻馬達有限元素分析模型 52
3.5.1 建立同步磁組馬達有限元素分析模型 52
3.5.2 Maxwell 2D 模型建立同步磁阻馬達 53
3.5.3 ANSYS功能設定 56
3.5.4 同步磁阻馬達飽和現象 61
3.5.5 電感與電流之變化關係 62
3.6 同步磁阻馬達控制系統 63
3.6.1 傳統PI控制與有限元素分析之每安培最大轉矩 63
3.6.2 速度比例積分控制器設計 68
3.6.3 步階回歸控制系統與智慧型步階回歸控制系統 70
第四章步階回歸位置控制 74
4.1 前言 74
4.2 步階回歸位置控制系統 75
4.2.1 步階回歸位置控制 75
4.2.2 步階回歸位置控制穩定性證明 77
第五章同步磁阻馬達智慧型步階回歸控制 79
5.1 前言 79
5.2 多項式派翠類神經網路之架構 80
5.3 多項式派翠類神經網路之穩定性分析 83
第六章 Simplorer分析與模擬結果 88
6.1 簡介 88
6.2 Simplorer建模和應用 89
6.2.1 ECE簡介與建模設定 89
6.2.2 Simplorer與ECE設定 93
6.2.3 Simplorer與C語言的聯合分析 94
6.3 控制系統於Simplorer分析之模擬結果 96
6.3.1 命令為二階方波於Simplorer分析結果 98
6.3.2 命令為正弦波於Simplorer分析結果 104
第七章實驗結果與討論 110
7.1 前言 110
7.2 實驗結果 113
7.2.1 命令為二階方波之結果 113
7.2.2 命令為正弦波之結果 120
7.3 實驗結果討論 124
7.4 效率以及頻寬量測 126
第八章結論與未來展望 133
8.1 結論 133
8.2 未來展望 134
參考文獻 135
作者簡歷 146

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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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