應用於內藏式永磁同步馬達驅動系統之智慧型高效能控制及小波共振頻率偵測

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林俊儒(Jyun-Ru Lin) 查詢紙本館藏

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電機工程學系

論文名稱

應用於內藏式永磁同步馬達驅動系統之智慧型高效能控制及小波共振頻率偵測
(Intelligent High-Performance Control for IPMSM Drive System and Detection of Resonant-frequency with Wavelet)

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至系統瀏覽論文 (2025-8-1以後開放)

摘要(中)

本文提出一具有每安培最大轉矩控制和弱磁控制的高效能內藏式永磁同步馬達驅動系統。由於內藏式永磁同步馬達的控制性能會因為溫度變化和磁飽和效應而非線性變化，因此提出一基於非對稱歸屬函數之派翠機率模糊類神經網路的智慧型每安培最大轉矩控制。本文詳細探討非對稱歸屬函數之派翠機率模糊類神經網路架構和如何訓練以得到內藏式永磁同步馬達的dq軸電感差值。首先，推導出可以實現內藏式永磁同步馬達的每安培最大轉矩控制的d軸電流命令。接著，透過非對稱歸屬函數之派翠機率模糊類神經網路獲得內藏式永磁同步馬達的dq軸電感差值，並將其代入每安培最大轉矩控制的d軸電流命令以減少磁飽和效應。此外，為弱磁控制設計了一個電壓控制迴路，該迴路在高速下可將變頻器輸出電壓限制在能夠輸出的最大電壓內。為使內藏式永磁同步馬達驅動系統在參數變化和外來干擾的影響下具備強健之控制性能，本文提出了適應性互補式滑動模式速度控制器以獲得更好的速度響應。最後，提出一些實驗結果以證明所提出的高效能控制策略的有效性。
　　本文亦發展一種基於小波多重解析度濾波器的共振頻率偵測架構，先獲取轉子速度誤差中共振頻率的特徵，接著利用設計之二階帶通濾波器掃頻找出內藏式永磁同步馬達驅動系統之共振頻率。實驗所使用之設備為應用德州儀器公司生產之浮點數數位訊號處理器TMS320F28075之內藏式永磁同步馬達驅動系統。

摘要(英)

A high-performance interior permanent magnet synchronous motor (IPMSM) drive system with maximum torque per ampere (MTPA) control and flux-weakening (FW) control is developed in this study. Since the control performance of IPMSM will vary nonlinearly owing to the temperature variation and magnetic saturation, an intelligent MTPA control based on a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) is proposed. The network structure and the training of the PPFNN-AMF to obtain the difference value of the dq-axis inductances of the IPMSM will be discussed in detail. First, the d-axis current command which can achieve the MTPA control of the IPMSM will be derived. Then, the difference value of the dq-axis inductances of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high speed, is designed for the FW control. In order to make the IPMSM servo drive system have robust control performance under parameter variations and external disturbances, an adaptive complementary sliding mode (ACSM) speed controller is proposed in this study to obtain a better speed response. Finally, some experimental results are given to demonstrate the effectiveness of the proposed high-performance control strategies.
A resonant-frequency detection scheme based on wavelet multiresolution filter is also developed in this study. Obtain the characteristic of the resonant-frequency among the rotor speed error first, and then use the designed second-order band-pass filter (BPF) to proceed frequency-sweeping to find the resonant-frequency of the IPMSM servo drive system. The equipment used in the experiment is an IPMSM servo drive system with the floating-point digital signal processor (DSP) TMS320F28075 produced by Texas Instruments.

關鍵字(中)

★ 內藏式永磁同步馬達
★ 每安培最大轉矩控制
★ 弱磁控制
★ 非對稱歸屬函數之派翠機率模糊類神經網路
★ 適應性互補式滑動模式控制
★ 小波轉換
★ 共振頻率偵測

關鍵字(英)

★ Interior permanent magnet synchronous motor (IPMSM)
★ Maximum torque per ampere (MTPA) control
★ Flux-weakening (FW) control
★ Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF)
★ Adaptive complementary sliding mode (ACSM) control
★ Wavelet transform (WT)
★ Resonant-frequency detection

論文目次

摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 XIV
第一章緒論 1
1.1 研究動機 1
1.2 文獻回顧與簡介 2
1.3 本文貢獻 7
1.4 論文大綱 7
第二章內藏式永磁同步馬達變頻驅動器硬體介紹 9
2.1 簡介 9
2.2 變頻器 9
2.3 磁粉式煞車 10
2.4 數位訊號處理器 11
2.5 驅動控制電路板 14
第三章內藏式永磁同步馬達數學模型及電磁轉矩方程式 19
3.1 前言 19
3.2 三相座標轉換 20
3.3 內藏式永磁同步馬達在abc座標系下之數學模型 23
3.4 內藏式永磁同步馬達在αβ座標系下之數學模型 26
3.5 內藏式永磁同步馬達在d-q座標系下之數學模型 29
3.6 凸極式反電動勢定義 32
第四章非對稱歸屬函數之派翠機率模糊類神經網路 35
4.1 前言 35
4.2 非對稱歸屬函數之派翠機率模糊類神經網路架構 35
4.3 線上學習法則 39
4.4 網路收斂性 41
第五章適應性互補式滑動模式速度控制器 43
5.1 前言 43
5.2 適應性互補式滑動模式速度控制器 43
5.3 適應性互補式滑動模式速度控制器穩定性證明 45
第六章內藏式永磁同步馬達智慧型每安培最大轉矩控制及弱磁控制 49
6.1 前言 49
6.2 內藏式永磁同步馬達之每安培最大轉矩公式推導 49
6.3 dq軸電感差值 50
6.3.1 永磁磁通估測 50
6.3.2 dq軸電感差值估測 50
6.4 內藏式永磁同步馬達之弱磁控制 52
6.5 內藏式永磁同步馬達智慧型每安培最大轉矩控制及弱磁控制 53
第七章模擬與實驗結果 55
7.1 前言 55
7.2 非對稱歸屬函數之派翠機率模糊類神經網路訓練結果 56
7.3 智慧型每安培最大轉矩控制及弱磁控制 58
7.3.1 模擬結果 58
7.3.2 實驗結果 76
第八章小波共振頻率偵測 94
8.1 前言 94
8.2 簡介 94
8.3 連續小波轉換 95
8.4 離散小波轉換 96
8.5 多重解析度分析 97
8.6 尺度函數 98
8.7 多貝西小波 100
8.8 小波共振頻率偵測流程 102
8.9 動態訊號分析儀 105
8.10 小波共振頻率偵測結果 107
第九章結論與未來研究方向 121
參考文獻 123
作者簡歷 131

參考文獻

[1] 彭文陽，「先進馬達與驅動技術專輯」，機械工業雜誌，434期，2019年5月號
[2] P. Pillay, and R. Krishnan, “Application characteristics of permanent magnet synchronous and brushless dc motors for servo drives,” IEEE Trans. Ind. Appl., vol. 27, no. 5, pp. 986-996, Sep./Oct. 1991.
[3] H. Kim, J. Son, and J. Lee, “A high-speed sliding-mode observer for the sensorless speed control of a PMSM,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4069-4077, Sep. 2011.
[4] S. Kim, Y. D. Yoon, S. K. Sul, and K. Ide, “Maximum torque per ampere (MTPA) control of an IPM machine based on signal injection considering inductance saturation,” IEEE Trans. Power Electron., vol. 28, no. 1, pp. 488-497, Jan. 2013.
[5] 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.
[6] J. Lemmens, P. Vanassche, and J. Driesen, “PMSM drive current and voltage limiting as a constraint optimal control problem,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 3, no. 2, pp. 326-338, Jun. 2014.
[7] 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.
[8] H. Pairo and A. Shoulaie, “Operating region and maximum attainable speed of energy-efficient control methods of interior permanent-magnet synchronous motors,” IET Power Electron., vol. 10, no. 5, pp. 555-567, Apr. 2017.
[9] S. Jung, J. Hong, and K. Nam, “Current minimizing torque control of the IPMSM using Ferrari’s method,” IEEE Trans. Power Electron., vol. 28, no. 12, pp. 5603-5617, Dec. 2013.
[10] 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, 2017.
[11] 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.
[12] Q. Liu and K. Hameyer, “High-performance adaptive torque control for an IPMSM with real-time MTPA operation,” IEEE Trans. Energy Convers., vol. 32, no. 2, pp. 571-581, Jun. 2017.
[13] W. Yu, and X. Li, “Fuzzy identification using fuzzy neural networks with stable learning algorithms,” IEEE Trans. Fuzzy Syst., vol. 12, no. 3, pp. 411-420, Jun. 2004.
[14] F. J. Lin, H. J. Shieh, P. K. Huang, and L. T. Teng, “Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 53, no. 9, pp. 1649-1661, Sep. 2006.
[15] F. J. Lin, P. H. Chou, C. S. Chen, and Y. S. Lin, “DSP-based cross-coupled synchronous control for dual linear motors via intelligent complementary slidingmode control,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1061-1073, Feb. 2012.
[16] H. Chaoui, and P. Sicard, “Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1123-1133, Feb. 2012.
[17] M. A. Khanesar, E. Kayacan, M. Teshnehlab, and O. Kaynak, “Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation,” IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4443-4455, Nov. 2012.
[18] S. J. Hart, R. E. Shaffer, S. L. Rose-Pehrsson, and J. R. McDonald, “Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys,”IEEE Trans. Geosci. Remote Sens., vol. 39, pp. 797-804, Jun. 2001.
[19] X. Jin, D. Srinivasan, and R. L. Cheu, “Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks,” IEEE Trans. Neural Netw., vol. 12, no. 5, pp. 1173-1187, September, 2001.
[20] F. J. Lin, K. C. Lu, T. H. Ke, and H. Y. Li, “Reactive power control of three-phase PV system during grids faults using Takagi-Sugeno-Kang probabilistic fuzzy neural network control,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5561-5528, Sep. 2015.
[21] R. J. Wai and R. Muthusamy, “Design of fuzzy-neural-network inherited backstepping control for robot manipulator including actuator dynamics,” IEEE Trans. Fuzzy Syst., vol. 22, no. 4, pp. 709-722, Aug. 2014.
[22] F. J. Lin, M. S. Huang, P. Y. Yeh, H. C. Tsai, and C. H. Kuan, “DSP-based probabilistic fuzzy neural network control for li-ion battery charger,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3782-3794, Aug. 2012.
[23] K. H. Tan, “Squirrel-cage induction generator system using wavelet Petri fuzzy neural network control for wind power applications,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5242-5254, Jul. 2016.
[24] K. Kaur, R. Rana, N. Kumar, M. Singh, and S. Mishra, “A colored petri net based frequency support scheme using fleet of electric vehicles in smart grid environment,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 4638-4649, Nov. 2016.
[25] P. Wang, L. Ma, R. M. P. Goverde, and Q. Wang, “Rescheduling trains using Petri nets and heuristic search,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 3, pp.726-735, Mar. 2016.
[26] Y. C. Hung, F. J. Lin, J. C. Hwang, J. K. Chang, and K. C. Ruan, “Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution,” IEEE Trans. Power Electron., vol. 30, no. 4, pp. 2350-2362, Apr. 2015.
[27] Y. Wang, Y. Feng, X. Zhang, and J. Liang, “A new reaching law for antidisturbance sliding-mode control of PMSM speed regulation system,” IEEE Trans. Power Electron., vol. 35, no. 4, pp. 4117-4126, Apr. 2020.
[28] A. Wang and S. Wei, “Sliding mode control for permanent magnet synchronous motor drive based on an improved exponential reaching law,” IEEE Access, vol. 7, pp. 146866-146875, 2019.
[29] H. Jin and X. Zhao, “Approach angle-based saturation function of modified complementary sliding mode control for PMLSM,” IEEE Access, vol. 7, pp. 126014-126024, 2019.
[30] F. J. Lin, Y. C. Hung, and M. T. Tsai, “Fault-tolerant control for six-phase PMSM drive system via intelligent complementary sliding-Mode control using TSKFNN-AMF,” IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5747-5762, Dec. 2013.
[31] C. Y. Liang and J. P. Su, “A new approach to the design of a fuzzy sliding mode controller,” Fuzzy Sets and Systems, vol. 139, no. 1, pp. 111-124, Oct. 2003.
[32] E. Kim, J. Kim, H. T. Nguyen, H. H. Choi, and J. Jung, “Compensation of parameter uncertainty using an adaptive sliding mode control strategy for an interior permanent magnet synchronous motor drive,” IEEE Access, vol. 7, pp. 11913-11923, 2019.
[33] H. H. Choi, N. T. T. Vu, and J. W. Jung, “Digital implementation of an adaptive speed regulator for a PMSM,” IEEE Trans. Power Electron., vol. 26, no. 1, pp. 3-8, Jan. 2011.
[34] G. Schoonhoven and M. Nasir Uddin, “MTPA- and FW-based robust nonlinear speed control of IPMSM drive using Lyapunov stability criterion,” IEEE Trans. Ind. Appl., vol. 52, no. 5, pp. 4365-4374, Sep./Oct. 2016.
[35] T. Miyajima, H. Fujimoto and M. Fujitsuna, "A Precise model-based design of voltage phase controller for IPMSM," IEEE Trans. Power Electron., vol. 28, no. 12, pp. 5655-5664, Dec. 2013.
[36] S. Bolognani, S. Calligaro and R. Petrella, “Adaptive flux-weakening controller for interior permanent magnet synchronous motor drives,” IEEE Journal of Emerging and Selected Topics in Power Electron., vol. 2, no. 2, pp. 236-248, Jun. 2014.
[37] Y. Chen, X. Huang, J. Wang, F. Niu, J. Zhang, Y. Fang and L. Wu, “Improved flux-weakening control of IPMSMs based on torque feedforward technique,” IEEE Trans. Power Electron., vol. 33, no. 12, pp. 10970-10978, Dec. 2018.
[38] S. M. Yang, and S. C. Wang, “The detection of resonance frequency in motion control systems,” IEEE Trans. Ind. Appl., vol. 50, no. 5, pp. 3423–3427, Sep./Oct. 2014.
[39] S. H. Kia, H. Henao, and G. A. Capolino, “Torsional vibration assessment using induction machine electromagnetic torque estimation,” IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 209–219, Jan. 2010.
[40] A. R. Mohanty, and C. Kar, “Fault detection in a multistage gearbox by demodulation of motor current waveform,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1285–1297, Aug. 2006.
[41] Y. Wang, Q. Zheng, and H. Zhang, “Adaptive control and predictive control for torsional vibration suppression in helicopter/engine system,” IEEE Access, vol. 6, pp. 23896–23906, Apr. 2018.
[42] M. J. Devaney, L. Eren, “Detecting motor bearing faults,” IEEE Instrum. Meas. Mag., vol. 7, no. 4, pp. 30-50, Dec. 2004.
[43] C. I. Chen, Y. C. Chen, “Intelligent identification of voltage variation events based on IEEE Std 1159-2009 for SCADA of distributed energy System,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2604-2611, Apr. 2015.
[44] C. I. Chen, “Virtual multifunction power quality analyzer based on adaptive linear neural network,” IEEE Trans. Ind. Electron., vol. 59, no. 8, pp. 3321-3329, Aug. 2012.
[45] Z. Zhao, C. Liu, Y. Li, Y. Li, J. Wang, B. S. Lin, J. Li, “Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks,” IEEE Access., vol. 7, no. 4, pp. 34060-34067, Feb. 2019.
[46] P. Sangeetha, S. Hemamalini, “Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor,” IET Signal Process., vol. 11, no. 5, pp. 604-612, Jul. 2017.
[47] S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674-693, Jul. 1989.
[48] M.T. Hanna, S.A. Mansoori, “The discrete time wavelet transform: its discrete time Fourier transform and filter bank implementation,” IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., vol. 2, pp. 180-183, 2001.
[49] J. A. R. Macias, A. G. Exposito, “Efficient computation of the running discrete Haar transform,” IEEE Trans. Power Del., vol. 21, no. 1, pp. 504-5, 2006
[50] Y. Y. Hong, C. W. Wang, “Switching detection/classification using discrete wavelet transform and self-organizing mapping network,” IEEE Trans. Power Del., vol. 20, no. 2, pp. 1662-1668, Apr. 2005.
[51] 盛暘科技股份有限公司，數位訊號處理，2007。
[52] Texas Instruments, TMS320F28075 datasheet.
[53] MCP4922 datasheet.
[54] 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」，中央大學電機工程系，碩士論文，民國106年6月。
[55] 周孝澤，「應用於內藏式永磁同步馬達之智慧型慣量估測及共振頻率偵測」，中央大學電機工程系，碩士論文，民國108年6月。
[56] 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。
[57] K. Ahsanullah, R. Dutta, and M. F. Rahman, “Analysis of low-speed IPMMs with distributed and fractional slot concentrated windings for wind energy applications,” IEEE Trans. Magn., vol. 53, no. 11, pp. 1-10, Nov. 2017
[58] 陳航生，「內藏式永磁同步馬達之特性分析及其電動機車之應用」，中央大學電機工程系，碩士論文，民國93年5月。
[59] 劉昌煥，「交流電機控制」，東華書局，民國92年。
[60] 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。
[61] 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 00, pp. 1814-1819, 2000.
[62] J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
[63] S. Mallat, “Multifrequency channel decompositions of images and wavelet models, ” IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 12, pp. 2091-2110, Dec. 1987.
[64] Y. Y. Hong, and C. W. Wang, “Switching detection/classification using discrete wavelet transform and self-organizing mapping network,” IEEE Trans. Power Del., vol. 20, no. 2, pp. 1662-1668, Apr. 2005.
[65] A. Houari, A. Bouabdallah, A. Djerioui, M. Machmoum, F. Auger, A. Darkawi, J. C. Olivier, and M. F. Benkhoris, “An effective compensation technique for speed smoothness at low-speed operation of PMSM drives,” IEEE Trans. Ind. Appl., vol. 54, no. 1, pp. 647-654, Apr. 2018.
[66] Keysight 35670A, datasheet.
[67] S. Kim, Y. Yoon, S. Sul and K. Ide, “Maximum torque per ampere (MTPA) control of an IPM machine based on signal injection considering inductance saturation,” IEEE Trans. Power Electron., vol. 28, no. 1, pp. 488-497, Jan. 2013.
[68] N. V. Nenkov, E. Zdravkova, “Implementation of a neural network using simulator and Petri nets,” (IJACSA) International Journal of Advanced Computer Science and Applications., vol. 7, no. 1, pp. 412–417, Sep. 2001.
[69] W. M. Lin, C. H. Lin, Z. C. Sun, “Adaptive multiple fault detection and alarm processing for loop system with probabilistic network,” IEEE Trans. Power Del., vol. 19, no. 1, pp. 64-69, Jan. 2004.

指導教授

林法正(Faa-Jeng Lin)

審核日期

2020-8-13

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