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

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姓名

劉映岑(Ying-Tsen Liu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

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
(Intelligent Speed Controller with Optimal Bandwidth for IPMSM Drive System)

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摘要(中)

本論文主要研究目的為利用提出之雙輸入雙輸出小波模糊類神經網路控制器結合新型每安培最大轉矩控制法應用於內藏式永磁同步馬達驅動系統以改善馬達效率，並藉由加入改良型干擾轉矩觀測器改善在週期性變動負載干擾下之速度響應。為使馬達控制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，使用本論文提出之雙輸入雙輸出小波模糊類神經網路控制器，線上調整馬達參數，再透過改良型干擾轉矩觀測器提供前饋補償控制力。新型每安培最大轉矩控制法是以磁場導向控制法為基礎來發展，藉由訊號注入之概念將訊號注入至電流角度上造成功率擾動進而提供電流角度讓馬達運作在最佳操作點上。本論文亦提出具線上增益調變之最佳速度迴路頻寬之馬達伺服驅動器架構，同樣利用提出之雙輸入雙輸出小波模糊類神經網路線上調整參數來改善頻寬大小。最後透過微芯公司(Microchip)所生產之數位訊號處理器實現內藏式永磁同步馬達驅動系統，透過模擬以及在測試平台上實測並驗證其有效性、可行性和在負載轉矩干擾下擁有良好的速度響應。

摘要(英)

A novel maximum torque per ampere (MTPA) method based on power perturbation for a field-oriented control (FOC) interior permanent magnet synchronous motor (IPMSM) drive system is proposed in this study. The proposed MTPA method, which is parameter independent and can improve the motor operation at both start-up and low speed, is designed based on the power perturbation by using the signal injection in the current angle. Moreover, the influence of current and voltage harmonics to the MTPA control can be eliminated effectively. Furthermore, to enhance the robustness of the control system, an online tuning scheme for an integral-proportional (IP) controller using a new wavelet fuzzy neural network (WFNN) with disturbance torque feedforward control is developed where the disturbance torque is obtained from an improved disturbance torque observer also proposed in this study. In order to achieve an optimal bandwidth, a novel online auto-tuning technique also using the new wavelet fuzzy neural network (WFNN) for a field-oriented control (FOC) interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this study. Finally, some experimental results using an IPMSM drive system based on a low price digital signal processor (DSP) are presented. From the experimental results, the proposed control approach can guarantee the control performance of speed loop even under a cyclic fluctuating load.

關鍵字(中)

★ 內藏式永磁同步馬達
★ 雙輸入雙輸出小波類神經網路
★ 每安培最大轉矩控制
★ 頻寬最佳化

關鍵字(英)

★ Interior permanent magnet synchronous motor(IPMSM)
★ wavelet fuzzy neural network(WFNN)
★ maximum torque per ampere control(MTPA)
★ optimal bandwidth

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XI
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 論文大綱 6
第二章內藏式永磁同步馬達變頻驅動器硬體介紹 7
2.1 前言 7
2.2 馬達變頻驅動系統 7
2.3 改良式磁粉式煞車 8
2.4 變頻驅動控制電路板 9
2.5 數位訊號處理器 13
第三章內藏式永磁同步馬達數學模型及電磁轉矩程式 17
3.1 前言 17
3.2 三相座標轉換 19
3.3 內藏式永磁同步馬達在abc座標系下之數學模型 22
3.4 內藏式永磁同步馬達在αβ座標系下之數學模型 25
3.5 內藏式永磁同步馬達在d-q座標系下之數學模型 28
3.6 凸極式反電動勢定義 31
第四章雙輸入雙輸出小波模糊類神經網路線上調整馬達參數 34
4.1 前言 34
4.2 雙輸入雙輸出小波模糊類神經網路之描述 34
4.3 線上學習法則 37
4.4 網路收斂性分析 40
第五章功率型每安培最大轉矩控制及改良型干擾轉矩觀測器 43
5.1 前言 43
5.2 功率型每安培最大轉矩控制 44
5.3 改良型干擾轉矩觀測器 50
第六章具線上增益調變之最佳速度迴路頻寬之馬達伺服驅動器 53
6.1 前言 53
6.2 內藏式永磁同步馬達驅動系統之轉移函數 53
6.3 內藏式永磁同步馬達驅動系統之動態性能分析 55
6.3.1 快速響應性能 55
6.3.2 相對穩定性分析 56
第七章模擬與實驗結果 58
7.1 前言 58
7.2 具智慧型線上增益調變之控制器結合功率型每安培最大轉矩控制及改良型干擾轉矩控制器 59
7.2.1 系統設計 59
7.2.2 功率型每安培最大轉矩控制結合改良型干擾轉矩觀測器模擬結果 60
7.2.3 功率型每安培最大轉矩控制之實驗結果 65
7.2.4 結合功率型每安培最大轉矩控制及改良型干擾轉矩觀測器之實驗結果 69
7.2.5 具智慧型線上增益調變之控制器結合功率型每安培最大轉矩控制及改良型干擾轉矩控制器之模擬結果 73
7.2.6 具智慧型線上增益調變之控制器結合功率型每安培最大轉矩控制及改良型干擾轉矩控制器之實驗結果 79
7.3 具線上增益調變之最佳速度迴路頻寬之馬達驅動器 83
7.3.1 系統設計 83
7.3.2 具線上增益調變之最佳速度迴路頻寬之馬達驅動器之模擬結果 85
7.3.3 具線上增益調變之最佳速度迴路頻寬之馬達驅動器之實驗結果 90
第八章結論與未來研究方向 95
參考文獻 96
作者簡歷 101

參考文獻

[1] 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.
[2] 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.
[3] T. Senjyu, T. Shingaki, and K. Uezato, “Sensorless vector control of synchronous reluctance motors with disturbance torque observer,” IEEE Trans. Ind. Electron., vol. 48, no. 2, pp. 402-407, Apr. 2001.
[4] N. Bianchi, M. Degano, and E. Fornasiero, “Sensitivity analysis of torque ripple reduction of synchronous reluctance and interior PM motors,” IEEE Trans. Ind. Appl., vol. 51, no. 1, pp. 187–195, Jan./Feb. 2015.
[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] R. Antonello, M. Carraro, and M. Zigliotto, “Maximum-torque-per-ampere operation of anisotropic synchronous permanent-magnet motors based on extremum seeking control,: IEEE Trans. Ind. Electron., vol. 61, no. 9, pp. 5086–5093, Sep. 2014.
[9] A. Ahmed, Y. Sozer, and M. Hamdan, “Maximum torque per ampere control for interior permanent magnet motors using DC link power measurement,” in Proc. IEEE Appl. Power Electron. Conf., pp. 826–832, 2014.
[10] S. Bolognani, R. Petrella, A. Prearo, and L. Sgarbossa, “Automatic tracking of MTPA trajectory in IPM motor drives based on AC current injection,” IEEE Trans. Ind. Appl., vol. 47, no. 1, pp. 105–114, Jan./Feb. 2011.
[11] S. Bolognani, L. Sgarbossa, A. Prearo, and R. Petrella, “On-line tracking of the MTPA trajectory in IPM motors via active power measurement,” in Proc. Int. Conf. Elect. Mach., pp. 1–7, 2010.
[12] T. Sun, and J. Wang, “Extension of Virtual Signal Injection Based MTPA Control for Interior Permanent Magnet Synchronous Machine Drives into Field Weakening Region,” IEEE Trans. Ind. Electron., vol. 62, no. 11, pp. 6809–6817, Nov. 2015.
[13] T. D. Do, S. Kwak, H. H. Choi, and J. W. Jung, “Suboptimal control scheme design for interior permanent magnet synchronous motors: An SDRE-based approach,” IEEE Trans. Power Electron., vol. 29, no. 6, pp. 3020–3031, Jun. 2014.
[14] Y. X. Su, C. H. Zheng, and B. Y. Duan, “Automatic disturbances rejection controller for precise motion control of permanent-magnet synchronous motors,” IEEE Trans. Ind. Electron., 2005, vol. 52, no. 3, pp. 814–823, Jun. 2005.
[15] J. Li, H. P. Ren, Y. R. Zhong “Robust speed control of induction motor drives using first-order auto-disturbance rejection controllers,” IEEE Trans. Ind. Appl., vol. 51, no. 1, pp. 712–720, Jan./Feb. 2015.
[16] 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.
[17] W. Li, and Y. Hori, “Vibration suppression using single neuron-based PI fuzzy controller and fractional-order disturbance observer,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 117–126, Feb. 2007.
[18] W. S. Huang, C. W. Liu, P. L. Hsu, and S. S. Yeh, “Precision control and compensation of servomotors and machine tools via the disturbance observer,” IEEE Trans. Ind. Electron., vol. 57, no. 1, pp. 420–429, Jan. 2010.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] J. Zhang, G. G. Walter, Y. Miao, and W. N. W. Lee, “Wavelet neural networks for function learning,” IEEE Trans. Signal Process., vol. 43, no. 6, pp. 1485–1497, Jun. 1995.
[25] H. Pan and L. Z. Xia, “Efficient object recognition using boundary representation and wavelet neural network,” IEEE Trans. Neural Netw., vol. 19, no. 12, pp. 2132–2149, Dec. 2008.
[26] C. H. Lu, “Wavelet fuzzy neural networks for identification and predictive control of dynamic systems,” IEEE Trans. Ind. Electron., vol. 58, no. 7, pp. 3046–3058, Jul. 2011.
[27] F. J. Lin, K. H. Tan, and J. H. Chiu, “Active islanding detection method using wavelet fuzzy neural network,” in Proc. IEEE Int. Conf. Fuzzy Syst., pp. 1–8., 2012.
[28] F. J. Lin, K. H. Tan, D. Y. Fang, and Y. D. Lee, “Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power,” IET Renew. Power Generat., vol. 7, no. 5, pp. 552–564, 2013.
[29] H. Wang, M. Yang, L. Niu, and D. Xu, “Current-loop bandwidth expansion strategy for permanent magnet synchronous motor drives,” in Proc. IEEE 5th Conf. Ind. Electron. Appl., pp. 1340–1345, 2010.
[30] A. Sarca, B. Naum, and D. Matianu, “A new approach for automatic tuning of electrical drives current loop controllers,” Advanced Topics in Electrical Engineering (ATEE), 2015 9th International Symposium, pp. 231-235, May 2015.
[31] J. Bocker, S. Beineke, and A. Bahr, “On the control bandwidth of servo drives,” in Proc. 13th Eur. Conf. Power Electron. Appl., pp. 1–10, 2009.
[32] C. J. Hsu and Y. S. Lai, “Novel On-Line Optimal Bandwidth Search and Auto Tuning Techniques for Servo Motor Drives, ” in proc. IEEE Energy Conversion Congress and Expo. (ECCE), PP. 1-8, 2016.
[33] L. Niu, D. Xu, M. Yang, X. Gui, and Z. Liu, “On-line inertia identification algorithm for PI parameters optimization in speed loop,” IEEE Trans. Power Electron., vol. 30, no. 2, pp. 849–858, Feb., 2015.
[34] 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。
[35] Microchip，MCP4922 datasheet.
[36] 瑞智精密股份有限公司, http://www.rechi.com
[37] 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。
[38] 劉昌煥，「交流電機控制」，東華書局，民國92年。
[39] Texas Instruments，AM26LS32ACN datasheet.
[40] 陳仕堯，「採用功率擾動之每安培最大轉矩控制內藏式永磁同步馬達驅動器之研製」，中央大學電機工程系，碩士論文，民國105年6月。
[41] F. J. Lin, R. J. Wai, and P. K. Huang, “Two-axis motion control system using wavelet neural network for ultrasonic motor drives,” IEE Proc. Electr. Power Appl., vol. 151, no. 5, pp. 613-621, Sep. 2004.
[42] S. J. Yoo, Y. H. Choi, J. B. Park, “Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: Adaptive learning rates approach,” IEEE Trans. Circuits Syst. I: Reg. Papers, vol. 53, no. 6, pp. 1381-1395, Jun. 2006.
[43] R. J. Wai, and C. M. Li, “Design of dynamic petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot,” IEEE Trans. Indust. Electron., vol. 56, no. 7, pp. 2667-2683, Jul. 2009.

指導教授

林法正(Faa-Jeng Lin)

審核日期

2017-8-18

推文