利用遞迴式勒壤得模糊類神經網路於永磁輔助同步磁阻馬達驅動系統之智慧型計算轉矩控制

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：88

、訪客IP：18.226.226.221

姓名

洪崇祐(Chung-Yu Hung) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用遞迴式勒壤得模糊類神經網路於永磁輔助同步磁阻馬達驅動系統之智慧型計算轉矩控制
(Intelligent Computed Torque Control of Permanent Magnet Assisted Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network)

相關論文

★ 機場地面燈光更新工程 -以桃園國際機場南邊跑滑道為例	★ 多功能太陽能微型逆變器之研製
★ 應用於儲能系統之智慧型太陽光電功率平滑化控制	★ 利用智慧型控制之三相主動式電力濾波器的研製
★ 應用於內藏式永磁同步馬達之智慧型速度控制及最佳伺服控制頻寬研製	★ 新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發
★ 同步磁阻馬達驅動系統之智慧型每安培最大轉矩追蹤控制	★ 利用適應性互補式滑動模態控制於同步磁阻馬達之寬速度控制
★ 具智慧型太陽光電功率平滑化控制之微電網電能管理系統	★ 高性能同步磁阻馬達驅動系統之寬速度範圍控制器發展
★ 智慧型互補式滑動模態控制系統實現於X-Y-θ三軸線性超音波馬達運動平台	★ 智慧型同動控制之龍門式定位平台及應用
★ 利用智慧型滑動模式控制之五軸主動式磁浮軸承控制系統	★ 智慧型控制雙饋式感應風力發電系統之研製
★ 無感測器直流變頻壓縮機驅動系統之研製	★ 應用於模組化輕型電動車之類神經網路控制六相永磁同步馬達驅動系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

本論文研究目的為研製與發展高性能永磁輔助同步磁阻馬達驅動系統，提出利用遞迴式勒壤得模糊類神經網路之計算轉矩控制法，以應對其非線性和時變特性。本論文首先介紹了使用有限元素分析法分析每安培最大轉矩控制，以獲得最佳的電流角命令，並將結果藉由查表法做應用。接著介紹計算轉矩控制法來追隨速度命令，但因系統存在總集不確定項很難事先得知，實際應用中難以實現。有鑒於此，提出結合了遞迴式勒壤得模糊類神經網路來近似計算轉矩控制。此外，為了補償遞迴式勒壤得模糊類神經網路可能的近似誤差，增加了一個自適應補償器，並利用李亞普諾夫穩定性理論推導，以保證遞迴式勒壤得模糊類神經網路線上學習法為漸進穩定。最後通過實驗結果驗證了所提出的遞迴式勒壤得模糊類神經網路之智慧型計算轉矩控制的有效性和強健性。
最後，本研究以32位元浮點運算數位訊號處理器TMS320F28075將所提出的智慧型控制實現於永磁輔助同步磁阻馬達驅動系統。

摘要(英)

An intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN) is proposed in this study to construct a high-performance PMASynRM drive system to confront its nonlinear and time-varying control characteristics. First, the dynamic model of a maximum torque per ampere (MTPA) controlled PMASynRM drive using ANSYS Maxwell-2D is introduced. The results of the finite element analysis (FEA) are made into a lookup table (LUT) to generate the current angle command of the MTPA. Then, a computed torque control (CTC) system is designed for the tracking of the speed reference. Since the detailed system dynamics including the uncertainty of PMASynRM drive system is unavailable in advance, it is very difficult to design an effective CTC in practical applications. Therefore, to alleviate the existed difficulties of the CTC, a recurrent Legendre fuzzy neural network (RLFNN) is proposed in this study to approximate the CTC. In addition, to compensate the possible approximated error of the RLFNN, an adaptive compensator is augmented. The online learning algorithms of the RLFNN are derived by using the Lyapunov stability method to assure asymptotical stability. Finally, the effectiveness and robustness of the proposed ICTCRLFNN controlled PMASynRM drive are verified by some experimental results.
Finally, the proposed intelligent control system and the vector mechanism for the PMASynRM drive are implemented using a 32-bit floating point digital signal processor (DSP) TMS320F28075.

關鍵字(中)

★ 永磁輔助同步磁阻馬達
★ 計算轉矩控制
★ 遞迴式勒壤得模糊類神經網路
★ 每安培最大轉矩
★ 有限元素分析

關鍵字(英)

★ permanent magnet assisted synchronous reluctance motor (PMASynRM)
★ computed torque control (CTC)
★ recurrent Legendre fuzzy neural network (RLFNN)
★ maximum torque per ampere (MTPA)
★ finite element analysis (FEA)

論文目次

摘要 I
Abstract II
目錄 IV
圖目錄 VII
表目錄 XI
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介紹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 11
2.3 功率級系統建構 12
2.3.1 前言 12
2.3.2 電容版設計 13
2.3.3 主功率級設計 14
2.3.4 閘級驅動設計 15
2.3.5 雙脈衝電路工作原理與結果分析 16
2.4 TMS320F28075數位訊號處理器控制板之電路 17
2.4.1 電壓源轉換電路 18
2.4.2 數位/類比轉換電壓準位轉換電路 19
2.5 輸入/輸出板之電路 19
2.5.1 ADC輸入腳位之過電流保護電路 20
2.5.2 過電流保護電路 21
2.5.3 數位/類比轉換電路 23
2.6 外部負載控制電路 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 30
3.3.1永磁輔助同步磁阻馬達在abc座標系下之數學模型 33
3.3.2永磁輔助同步磁阻馬達在座標系下之數學模型 35
3.3.3永磁輔助同步磁阻馬達在d-q座標系下之數學模型 39
3.3.4永磁輔助同步磁阻馬達反電動勢定義 41
3.4 馬達對位方法 44
3.4.1 同步磁阻馬達對位方法 44
3.4.2 永磁輔助同步磁阻馬達對位方法 47
3.5 增量型編碼器位置迴授和速度估算 49
3.6 電流比例積分控制器之設計 50
3.7 永磁輔助同步磁阻馬達控制架構 52
3.7.1 傳統每安培最大轉矩控制 52
3.7.2 有限元素分析每安培最大轉矩控制 55
3.7.3 利用遞迴式勒壤得模糊類神經網路之智慧型計算轉矩控制 57
第四章永磁輔助同步磁阻馬達之有限元素分析 59
4.1 前言 59
4.2 建立永磁輔助同步磁阻馬達有限元素分析模型 60
4.2.1 Maxwell 2D 模型建立永磁輔助同步磁阻馬達 61
4.2.2 ANSYS功能設定 62
4.3 Simplorer 模擬分析 65
4.3.3 ECE建模設定 66
4.3.4 模擬與實驗結果驗證 68
第五章永磁輔助同步磁阻馬達計算轉矩控制 69
5.1 前言 69
5.2 計算轉矩控制原理 70
5.3 計算轉矩控制穩定性證明 72
第六章利用遞迴式勒壤得模糊類神經網路之智慧型計算轉矩控制 73
6.1 前言 73
6.2 利用遞迴式勒壤得模糊類神經網路之智慧型計算轉矩控制系統 73
6.3 遞迴式勒壤得模糊類神經網路之架構 74
6.4 遞迴式勒壤得模糊類神經網路穩定性證明 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 98
8.1 結論 98
8.2 未來展望 98
參考文獻 99
作者簡歷 106

參考文獻

[1] A. T. D. Almeida, F. J. T. E. Ferreira and A. Q. Duarte, “Technical and Economical Considerations on Super High-Efficiency Three-Phase Motors,” IEEE Trans. Ind. Appl., vol. 50, no. 2, pp. 1274-1285, Apr. 2014.
[2] A. T. D. Almeida, F. J. T. E. Ferreira and G. Baoming, “Beyond Induction Motors Technology Trend to Move up Efficiency,” IEEE Trans. Ind. Appl., vol. 50, no. 3, pp. 2103-2114, May. 2014.
[3] A. Komura, Hitachi, "Development of IES High Efficiency Motor with Iron-base Amorphous Magnetic Cores," 2015. [Online]. Available: http://docplayer.net/52943274-Development-of-ie5-high-efficiency-motor-with-iron-base-amorphous-magnetic-cores.html
[4] Drive and control, "Siemens Enters the Synchronous Reluctance Fray." 2015. [Online]. Available:
https://drivesncontrols.com/news/fullstory.php/aid/4735/Siemens_enters the_synchronous_reluctance fray.html
[5] ABB, "Synchronous Reluctance Motor-drive Package for Machine Builders," 2014. [Online].
Available: https://search.abb.com/library/Download.aspx?DocumentID=3AUA0000120962&LanguageCode=en&DocumentPartId=1&Action=Launch
[6] G. Pellegrino, A. Vagati and P. Guglielmi, "Design Tradeoffs between Constant Power Speed Range, Uncontrolled Generator Operation, and Rated Current of IPM Motor Drives," IEEE Trans. Ind. Appl., vol. 47, no. 5, pp. 1995-2003, Oct. 2011.
[7] P. Niazi, H. A. Toliyat, Dal-Ho Cheong and Jung-Chul Kim, "a Low-cost and Efficient Permanent Magnet Assisted Synchronous Reluctance Motor Drive," in Proc. IEEE Int. Conf. Electr. Mach. Drives, San Antonio, Texas, USA, pp. 659-666, 2005.
[8] H. Cai, B. Guan and L. Xu, "Low-Cost Ferrite PM-Assisted Synchronous Reluctance Machine for Electric Vehicles," IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5741- 5748, 2014.
[9] R. Vartanian, H. A. Toliyat, B. Akin and R. Poley, "Power Factor Improvement of Synchronous Reluctance Motors (SynRM) Using Permanent Magnets for Drive Size Reduction," in Proc. 2012 Twenty-Seventh Annu. IEEE Appl. Power Electron. Conf. Expo. (APEC), Orlando, Florida, USA, pp. 628-633, 2012.
[10] EMACH, Ultra Premium Efficiency (IE5) PM-assisted Synchronous Reluctance Motors," [Online]. Available: http://emach.ru/ieS-pm-assisted/
[11] R. Vartanian, Y. Deshpande and H. A. Toliyat, "Performance Analysis of a Ferrite Based Fractional Horsepower Permanent Magnet Assisted SynM for Fan and Pump Applications," in Proc. 2013 Int. Electr. Mach. Drives Conf., Chicago, Illinois, USA, pp. 1405-1410, 2013.
[12] S. Taghavi and P. Pillay, "A Sizing Methodology of the Synchronous Reluctance Motor for Traction Applications," IEEE J. Emerging Sel. Top. Power Electron., vol. 2, no. 2, pp. 329-340, June 2014.
[13] H. Mahmoud and N. Bianchi, "Eccentricity in Synchronous Reluctance Motors -Part I: Analytical and Finite-Element Models," IEEE Trans. Energy Convers., vol. 30, no. 2, pp. 745-753, June 2015.
[14] H. Mahmoud and N. Bianchi, "Eccentricity in Synchronous Reluctance Motors-_Part II: Different Rotor Geometry and Stator Windings, " IEEE Trans. Energy Convers., vol. 30, no. 2, pp. 754-760, June 2015.
[15] Z. Zhang, R. Ma, L. Wang, and J. Zhang, “Novel PMSM control for anti-lock braking considering transmission properties of the electric vehicle, ” IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10378-10386, Nov. 2018.
[16] Z. Qiu, Y. Chen, Y. Kang, X. Liu, and F. Gu, “Investigation into periodic signal-based dithering modulations for suppression sideband vibro-acoustics in PMSM used by electric vehicles, ” IEEE Trans. Energy Convers, vol. 36, no. 3, pp. 1787-1796, Sep. 2021.
[17] S. Sriprang, B. Nahid-Mobarakeh, S. Pierfederici, N. Takorabet, N. Bizon , P.Kumam, P. Mungporn, and P. Thounthong, “Robust flatness control with extended Luenberger observer for PMSM drive, ” Proc. IEEE Transp. Electrific. Conf. Expo, Asia–Pacific, pp. 1–8, Jun. 2018.
[18] I. Boldea, L. Tutelea, and C. I. Pitic, “PM-assisted reluctance synchronous motor/generator (PM-RSM) for mild hybrid vehicles: Electromagnetic design,” IEEE Trans. Ind. Appl., vol. 40, no. 2, pp. 492–498, Mar.–Apr. 2004.
[19] S. Sriprang, B. Nahid-Mobarakeh, N. Takorabet, S. Pierfederici, P. Mungporn, P. Thounthong, N. Bizon, P.Kumam, and Z. Shah, “Maximum torque per ampere and field-weakening controls for the high-speed operation of permanent-magnet assisted synchronous reluctance motors,” In 2019 Research, Invention, and Innovation Congress (RI2C), pp. 1-7, 2005.
[20] J.-H. Lee, Y.-J. Jang, and J.-P. Hong, “Characteristic analysis of permanent magnet-assisted synchronous reluctance motor for high power application,” J. Appl. Phys., vol. 97, no. 10, pp. 10Q503-10Q503-3, May 2005.
[21] B. Kerdsup, N. Takorabet, and B. Nahidmobarakeh, “Design of permanent magnet-assisted synchronous reluctance motors with maximum efficiency-power factor and torque per cost,” 2018 XIII International Conference on Electrical Machines (ICEM), 2018.
[22] D.-H. Jung, Y. Kwak, J. Lee, and C.-S. Jin, “Study on the optimal design of PMa-SynRM loading ratio for achievement of ultra premium efficiency,” IEEE Trans. Magn., vol. 53, no. 6, pp. 1–4, Jun. 2017.
[23] F.-J. Lin, Y.-C. Hung, J.-M. Chen, and C.-M. Yeh, “Sensorless IPMSM drive system using saliency back-EMF based intelligent torque observer with MTPA control,” IEEE Trans. Ind Informat, vol. 10, no. 2, pp. 1226-1241, May 2014.
[24] T.-H. Liu, Y. Chen, M.-J. Wu, and B.-C. Dai, “Adaptive controller for an MTPA IPMSM drive system without using a high-frequency sinusoidal generator,” IET J. Eng., vol. 2017, no. 2, pp. 13–25, Feb. 2017.
[25] T. Sun, J. Wang, and X. Chen, “Maximum torque per ampere (MTPA) control for interior permanent magnet synchronous machine drives based on virtual signal injection,” IEEE Trans. Power Electron., vol. 30, no. 9, pp. 5036–5045, Sep. 2015.
[26] A. Dianov, A. Anuchin, and A. Bodrov, “Robust MTPA control for steady state operation of low-cost IPMSM drives,” IEEE J. Emerg. Sel. Top. Ind. Electron., early accessed, 2021.
[27] F.-J. Lin, Y.-H. Liao, J.-R. Lin, and W.-T. Lin, “Interior permanent magnet synchronous motor drive system with machine learning-based maximum torque per ampere and flux-weakening control,” Energies, vol. 14, no. 2, Jan. 2021.
[28] Y. I. Nadjai, H. Ahmed, N. Takorabet, and P. Haghgooei, “maximum torque per ampere control of permanent magnet assisted synchronous reluctance motor: an experimental study,” Int. J. Robot. Control Syst., vol. 1, no. 4, pp. 416–427, Jul. 2021.
[29] P. Niazi, H. A. Toliyat, and A. Goodarzi, “Robust maximum torque per ampere (MTPA) control of PM-assisted SynRM for traction application,” IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 1538–1545, Jul. 2007.
[30] F.-J. Lin and R.-J. Wai, “A hybrid computed torque controller using fuzzy neural network for motor-quick-return servo mechanism,” IEEE/ASME Trans. Mechatron., vol. 6, no. 1, pp. 75-89, Mar. 2001.
[31] F.-J. Lin and R.-J. Wai, “Hybrid computed torque controlled motor-toggle servomechanism using fuzzy neural network uncertainty observer,” Neurocomputing, vol. 48, pp. 403-422, 2002.
[32] J. C. Patra and C. Bornand, “Nonlinear dynamic system identification using Legendre neural network,” in Proc. Int. Joint Conf. Neural Netw. (IJCNN), Barcelona, Spain, pp. 1–7, 2010.
[33] D. M. Sahoo, and S. Chakraverty, “Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 48, no. 1, pp. 1–10, Jan. 2018.
[34] C.-H. Lin, “Novel adaptive modified recurrent Legendre neural network control for a PMSM servo-driven electric scooter with V-belt continuously variable transmission system dynamics,” Trans. Inst Meas. Control, vol. 37, no. 10, pp. 1181–1196, Nov, 2015.
[35] D. Chakraborty, and N. R. Pal, “Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm,” IEEE Trans. Syst. Man Cybern. B, vol. 31, no. 3, pp. 391–400, Jun. 2001.
[36] Y.-Y. Lin, J.-Y. Chang, and C.-T. Lin, “Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network,” IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 2, pp. 310–321, Feb. 2013.
[37] C.-H. Chen, C.-J. Lin, and C.-T. Lin, “A functional-link-based neuro-fuzzy network for nonlinear system control,” IEEE Trans. Fuzzy Syst., vol. 16, no. 5, pp. 1362–1378, Oct. 2008.
[38] F.-J. Lin, I.-F. Sun, K.-J. Yang, and J.-K. Chang, “Recurrent fuzzy neural cerebellar model articulation network fault-tolerant control of six-phase permanent magnet synchronous motor position servo drive,” IEEE Trans. Fuzzy Syst., vol. 24, no. 1, pp. 153–167, Feb. 2016.
[39] 陳世剛，“利用函數連結放射狀基底函數網路於適應性步階迴歸控制六相永磁同步馬達定位驅動系統”，碩士論文，國立中央大學電機系，民國一百零五年。
[40] 黃泰寅，“新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發”
碩士論文，國立中央大學電機系，民國一百零六年。
[41] TMS320F2807x Piccolo Microcontrollers Datasheet, Texas Instruments.
[42] Z. Chen, D. Boroyevich, P. Mattavelli and K. Ngo, "A frequency-domain study on the effect of DC-link decoupling capacitors," 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, pp. 1886-1893, 2013.
[43] 劉昌煥，「交流電機控制」，東華書局，民國92年。
[44] 高子胤，「以反電動勢為基礎之比例積分微分類神經網路估測器之無感測器變頻壓縮機驅動系統開發」，中央大學電機工程系，碩士論文，民國100年7月。
[45] 陳家銘，「以單一直流鏈電流感測器結合低轉速轉矩補償之無轉軸位置感測器變頻壓縮機驅動系統開發」，中央大學電機工程系，碩士論文，民國102年6月。
[46] Keysight 35670A, datasheet.
[47] Z. Chen, M. Tomita, S. Ichikawa, S. Doki, and S. Okuma, “Sensorless control of interior permanent magnet synchronous motor by estimator of an extented electromotive force,” Proc. IECON 2000, pp. 1814-1819, 2000.
[48] 吳長恩，「具寬速度控制範圍之同步磁阻馬達驅動器研製」，國立台北科技大學，碩士論文，民國105年7月。
[49] F.-J. Lin, Y.-T. Liu and W.-A. Yu, “Power perturbation based MTPA with an online tuning speed controller for an IPMSM drive system,” IEEE Trans. Ind. Electron., vol. 65, no. 5, pp. 3677–3687, May 2018.
[50] D. L. Logan, A First Course in the Finite Element Method, MA: Cengage Learning, 2017.
[51] A. Karvonen and T. Thiringer, ”Co-simulation and harmonic analysis of a hybrid vehicle traction system, ” 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), 2015.
[52] J. Wei, J. Chen, P. Liu and B. Zhou, “The optimized triloop control strategy of integrated motor-drive and battery-charging system Based on the split-field-winding doubly salient electromagnetic machine in driving mode, “IEEE Trans. Ind. Electron., vol. 68, no. 2, pp. 1769-1779, Feb. 2021.
[53] P. Kumar N. and T. B. Isha, “FEM based electromagnetic signature analysis of winding inter-turn short-circuit fault in inverter fed induction motor, “CES Trans. Electr. Mach. Syst., vol. 3, no. 3, pp. 309-315, Sept. 2019.
[54] A. K. Singh, P. Kumar, C. U. Reddy and K. Prabhakar, “Simulation of direct torque control of induction motor using Simulink, simplorer and maxwell software, “ 2015 IEEE International Transport. Electrific. Conference (ITEC), pp. 1-6, 2015.
[55] M. Jafarboland, M. Tashakorian and A. Shirzadi, “Simulation of electrical motor drive using simulink, simplorer and maxwell software, “ 2014 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 808-813, 2014.
[56] Alan Chen, “Introduction of ECE model in maxwell reduce order model generation for PMSM,” Mar. 2018.
[57] F. J. Lin, S. G. Chen, and C. W. Hsu, “Intelligent backstepping control using recurrent feature selection fuzzy neural network for synchronous reluctance motor position servo drive system,” IEEE Trans. Fuzzy Syst., vol. 27, no. 3, pp. 413–427, Mar. 2019.
[58] F. J. Lin, I F. Sun, K. J. Yang, and J. K. Chang, “Recurrent fuzzy neural cerebellar model articulation network fault-tolerant control of six-phase permanent magnet synchronous motor position servo drive,” IEEE Trans. Fuzzy Syst., vol. 24, no. 1, pp. 153–167, Feb. 2016.
[59] T. H. Liu, H. T. Pu, and C. K. Lin, “Implementation of an adaptive position control system of a permanent-magnet synchronous motor and its application,” IET Elect. Power Appl., vol. 4, no. 2, pp. 121–130, Feb. 2010.
[60] J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.

指導教授

林法正(Faa-Jeng Lin)

審核日期

2022-8-24

推文