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姓名	陳世剛(Shih-Gang Chen)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	高性能同步磁阻馬達驅動系統之 寬速度範圍控制器發展 (Development of Wide Speed Range Controllers for High-Performance Synchronous Reluctance Motor Drive)
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摘要(中)	近年來，同步磁阻馬達(Synchronous Reluctance Motor, SynRM)由於具備機械結構簡單、堅固耐用和低成本等特性，已經成功且廣泛地被設計並實現在各種應用之中。由於同步磁阻馬達不需要永磁材料(Permanent Magnetic Material)，鼠籠(Squirrel Cage)及激磁線圈(Excitation Winding)，所以轉子會比感應馬達和永磁同步馬達的轉子便宜，因此同步磁阻馬達成為可替代感應馬達和永磁同步馬達的另一選項。此外，由於同步磁阻馬達之轉子凸極(Saliency)較大且無退磁(Demagnetization)等問題，所以非常適用於高速應用，像是電動車、油電混合車和牽引應用等。有鑑於此，本論文之目標即為發展以數位訊號處理器(Digital Signal Processor, DSP)為基礎並具寬速度範圍操作(Wide Speed Range Operation)之同步磁阻馬達驅動系統。 本論文首先發展以數位訊號處理器TMS320F28075 為基礎之控制系統，並詳述同步磁阻馬達驅動系統之架構，再進行同步磁阻馬達動態模型、有限元素分析和實驗設置之描述。另外，由於未模式化動態的存在及磁飽和現象，同步磁阻馬達驅動系統可視為一高度非線性且時變的系統，因此，各種非線性速度控制器，像是結合計算電流控制(Computed Current Control)和適應控制(Adaptive Control)優點之適應性計算電流(Adaptive Computed Current, ACC）速度控制器；結合互補式滑動模態控制 (Complementary Sliding Mode Control)和適應控制優點之適應性互補式滑動模態 (Adaptive Complementary Sliding Mode, ACSM) 速度控制器，已提出用於控制同步磁阻馬達的轉速，並具備可調節馬達速度之強健性。此外，為了在定轉矩區域內能節省驅動同步磁阻馬達的能源，採用了智慧型每安培最大轉矩(Maximum Torque per Ampere, MTPA)追隨控制器和智慧型最大功率因數(Maximum Power Factor, MPF)搜尋控制器，並利用無模型控制(Model-Free Control)方法如遞迴式勒壤得模糊類神經網路(Recurrent Legendre Fuzzy Neural Network, RLFNN)和遞迴式切比雪夫模糊類神經網路(Recurrent Chebyshev Fuzzy Neural Network, RCFNN)…等。另外，透過新型的電壓角控制器，可以完成弱磁（Flux-Weakening, FW）和每伏特最大轉矩（Maximum Torque per Voltage, MTPV）控制來實現寬速度範圍之操作，因此，在定功率和降功率區域中，所提出之具有前饋項的電壓角控制器適用於具高度非線性之同步磁阻馬達驅動系統。更重要的是，由於目前還沒有一種更好的方法可以用來設計同步磁阻馬達的直軸電流命令，因此提出了一種線上最大功率因數控制器來實現最大功率因數和解決同步磁阻馬達的直軸電流命令設計問題。最後，根據實驗結果，所發展之高性能同步磁阻馬達驅動系統具有良好的控制性能和強健性，且可有效地應用於寬速度範圍操作。
摘要(英)	In recent years, the synchronous reluctance motors (SynRMs) have been designed and adopted in many applications due to their important features such as mechanically simple, rugged structure, and low cost. Since the SynRMs do not require the permanent magnetic material, squirrel cage, and excitation winding, the rotor is potentially cheaper than both the induction motors (IMs) and permanent magnet synchronous motors (PMSMs). Therefore, SynRMs become a viable alternative to replace the IMs and PMSMs. Moreover, since the presence of large rotor saliency and no demagnetization issue, it makes SynRMs very suitable for high-speed applications, such as electric vehicles (EVs), hybrid EVs, and traction applications. For the above reasons, the purpose of this dissertation is to develop a digital signal processor (DSP)-based wide speed range operation of SynRM drive system. At the beginning of this dissertation, the DSP-based control system using a 32-bit floating-point DSP, TMS320F28075, and the SynRM drive system are presented in detail. Then, the SynRM dynamic model, finite element analysis, and experimental setup are described. Moreover, due to the existed unmodeled dynamics and magnetic saturation phenomenon, the SynRM drive system is a highly nonlinear and time-varying system. Therefore, various nonlinear speed controllers such as adaptive computed current (ACC) speed controller, which combines the merits of the computed current control and adaptive control and adaptive complementary sliding mode (ACSM) speed controller, which combines the merits of complementary sliding mode control and adaptive control, have been proposed to control the rotor speed of the SynRM for the regulation of motor speed with robustness. Furthermore, in order to save the energy of SynRM drive in the constant torque region, the intelligent maximum torque per ampere (MTPA) tracking controller and intelligent maximum power factor (MPF) searching controller using the model-free control methods such as recurrent Legendre fuzzy neural network (RLFNN) and recurrent Chebyshev fuzzy neural network (RCFNN), are proposed in this dissertation. In addition, the flux-weakening (FW) and maximum torque per voltage (MTPV) can be carried out to achieve the wide speed range operation through a novel voltage angle controller. Therefore, the proposed voltage angle controller which possesses a feedforward term is suitable for the highly nonlinear SynRM drive system in the operation of constant power and reduced power regions. More importantly, since currently there is no superior way to design the d-axis current command of the SynRM drive, an online MPF controller is proposed to achieve the MPF and solve the problem of d-axis current command design of SynRM drive. Finally, according to the experimental results, the developed high-performance SynRM drive system possesses good control performance and robustness, and can apply to the wide speed range operation effectively.
關鍵字(中)	★ 同步磁阻馬達 ★ 適應性計算電流控制 ★ 適應性互補式滑動模態控制 ★ 每安培最大轉矩 ★ 最大功率因數 ★ 每伏特最大轉矩	關鍵字(英)	★ Synchronous reluctance motor ★ adaptive computed current control ★ adaptive complementary sliding mode control ★ maximum torque per ampere ★ maximum power factor ★ maximum torque per voltage
論文目次	摘 要 I Abstract III Acronyms V 誌 謝 VIII Contents IX List of Figures XI List of Tables XX Chapter 1 INTRODUCTION 1 1.1 Historical Background 1 1.2 Literature Review 6 1.3 Motivations 12 1.4 Organization 14 Chapter 2 DSP-BASED CONTROL SYSTEM, FEA OF SYNRM AND SYNRM DRIVE SYSTEM. 16 2.1 Overview 16 2.2 DSP-based Control System 17 2.2.1 Introduction of DSP TMS320F28075 17 2.2.2 DSP 28075 Control, I/O Extension and Encoder Interface Board 18 2.3 FEA of SynRM 21 2.3.1 Cross-Saturation and Saturation Analyses 23 2.3.2 Inductance and MTPA Analyses 26 2.4 SynRM Drive System 27 2.4.1 SynRM Control System 28 2.4.2 Experimental Setup 30 Chapter 3 INTELLIGENT MTPA TRACKING CONTROL OF SYNRM USING RLFNN 32 3.1 Overview 32 3.2 Modeling of SynRM Drive 33 3.3 TMTPA Control System 34 3.4 Intelligent MTPA Tracking Control System 38 3.4.1 ACC Speed Controller 38 3.4.2 Intelligent MTPA Tracking Controller 41 3.4.3 RLFNN Controller 47 3.4.4 Online Learning Algorithm 50 3.4.5 Convergence Analysis 53 3.5 Experimental Results 54 3.6 Summary 67 Chapter 4 INTELLIGENT MPF SEARCHING CONTROL USING RCFNN CURRENT ANGLE CONTROLLER 69 4.1 Overview 69 4.2 TMPFC System 70 4.3 Intelligent MPF Searching Control System 73 4.3.1 PI-Based Stator Resistance Estimator 73 4.3.2 Stator Flux Estimator 73 4.3.3 Intelligent MPF Searching Controller 74 4.3.4 RCFNN Current Angle Controller 77 4.3.5 Online Learning Algorithm for RCFNN 80 4.3.6 Convergence Analysis 83 4.4 Experimental Results 85 4.5 Summary 102 Chapter 5 ACSM CONTROL OF SYNRM WITH MPF, FW, AND MTPV 103 5.1 Overview 103 5.2 Conventional FW Control System 104 5.3 Intelligent MPF Searching Control System 107 5.3.1 ACSM Speed Controller 111 5.3.2 Mode Selector 114 5.3.3 Mode I-Current Control 117 5.3.4 Mode II-Voltage Control 119 5.4 Experimental Results 122 5.5 Summary 140 Chapter 6 CONCLUSIONS AND SUGGESTIONS FOR FUTURE WORKS 142 6.1 Conclusions 142 6.2 Suggestions for Future Works 143 Reference 144 VITA 153
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指導教授	林法正(Faa-Jeng Lin)	審核日期	2020-8-18
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