交流馬達因向量控制，又稱磁場導向控制(Field Oriented Control, FOC) 的提出，使得其控制性能得到巨大的提升，而逐漸成為工業界的主流，其中感應馬達因其堅固、耐用、價格低廉等優勢有著廣泛的應用。傳統中使用感測器感測的參數來進行控制，然而其成本且應用範圍的限制，使得無感測控制技術被提出，在實行此技術之前需確保估測器之準確性，故利用即時動態估測實現估測器之偵錯及診斷，其中以擴展型卡爾曼濾波器(Extended Kalman Filter, EKF) 為基礎所發展的估測器因具有雜訊免疫和即時估測能力而被廣泛的採用。
本論文利用擴展型卡爾曼濾波器實現感應馬達之即時動態估測模擬及實驗，以此來觀察其最佳轉速估測效能，故會比較以轉速為擴展項之五階估測器及以負載為擴展項之六階估測器在不同操作條件下的估測效能，另外因定子及轉子電阻的變化對於轉速估測會造成誤差，故比較以轉子或定子電阻為擴展項之七階估測器在長時間運作下的轉速估測效能，及以轉子及定子電阻為擴展項之八階估測器在長時間運作下的估測效能。

Due to the introduction of vector control, also known as Field Oriented Control (FOC), the control performance of the AC motor has greatly improved, and has gradually become the mainstream of the industry. Among them, the induction motor have a wide range of applications because of robustness, durability, and low price. Traditionally, sensor control of induction motor has some limitation like the cost and range of applications. Therefore, nonsensing control technology has been proposed. The accuracy of the estimator is the most important thing, so using the real-time estimation of the dynamics to achieve estimator’s debug and diagnosis. Extended Kalman Filter (EKF) is widely used due to its noise immunity
and real-time estimation capabilities.
In this paper, simulation and experiment of Real-time estimation of the dynamics in InductionMotors using Extended Kalman Filter is proposed. Therefore, the fifth-order estimator with speed as the expansion term will be compared with the sixth order estimator with load as an extension term under different operating conditions. In addition, the changes of the stator and rotor resistance will cause errors in the speed estimation. Therefore, compare the speed estimation performance of the seventh-order estimator with the rotor or stator resistance
as the extended term under long-term operation. Finally, the eighth-order estimator, which uses the rotor and stator resistances as extended items, will be proposed.

[1] 黃仲欽, 「電機機械理論」. 課程講義, 2015.
[2] G. Welch and G. Bishop, “An introduction to the kalman filter (tr95-041),” Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, pp. 27599–3175, 2006.
[3] S. Williamson and R. Healey, “Space vector representation of advanced motor models for vector controlled induction motors,” IEE Proceedings - Electric Power Applications, vol. 143, no. 1, pp. 69–77, 1996.
[4] A. Elrajoubi, S. S. Ang, and A. Abushaiba, “Tms320f28335 dsp programming using
matlab simulink embedded coder: Techniques and advancements,” in 2017 IEEE 18th
Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1–7, 2017.
[5] 謝仲翔, 「空間向量脈寬調變驅控之鼠籠式感應馬達動態建模」. 國立台灣科技大學機械工程學系，碩士論文, 2017.
[6] 劉昌煥, 「交流電機控制-向量控制與直接轉矩控制原理」. 東華書局, 2008.
[7] T. I. Europe, “Sensorless control with kalman filter on tms320 fixed-point dsp,” Literature number: BPRA057, 1997.
[8] S. Das, A. Pal, R. Kumar, and A. K. Chattopadhyay, “An improved rotor flux based model reference adaptive controller for four-quadrant vector controlled induction motor drives,” in TENCON 2015 - 2015 IEEE Region 10 Conference, pp. 1–6, 2010.
[9] Y. Bensalem and M. N. Abdelkrim, “A sensorless neural model reference adaptive control for induction motor drives,” in 2009 3rd International Conference on Signals, Circuits and Systems (SCS), pp. 1–6, 2010.
[10] S. Maiti, C. Chakraborty, Y. Hori, and M. C. Ta, “Model reference adaptive controllerbased rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power,” IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 594–601, 2008.
[11] R. Verma, V. Verma, and C. Chakraborty, “Ann based sensorless vector controlled induction motor drive suitable for four quadrant operation,” in Students’ 2014 IEEE Technology Symposium (TechSym), pp. 182–187, 2008.
[12] S. T. Nguyen, P. H. Pham, T. V. Pham, H. X. Ha, C. T. Nguyen, and P. C. Do, “A sensorless three-phase induction motor drive using indirect field oriented control and artificial neural network,” in 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1454–1459, 2008.
[13] M. Barut, S. Bogosyan, and M. Gokasan, “Experimental evaluation of braided ekf for sensorless control of induction motors,” IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 620–632, 2008.
[14] Z. Yin, G. Li, Y. Zhang, J. Liu, X. Sun, and Y. Zhong, “A speed and flux observer of induction motor based on extended kalman filter and markov chain,” IEEE Transactions on Power Electronics, vol. 32, no. 9, pp. 7096–7117, 2017.
[15] M. Barut, S. Bogosyan, and M. Gokasan, “Speed-sensorless estimation for induction motors using extended kalman filters,” IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 272–280, 2007.
[16] Y. Zhang, Z. Zhao, T. Lu, L. Yuan, W. Xu, and J. Zhu, “A comparative study of luenberger observer, sliding mode observer and extended kalman filter for sensorless vector control of induction motor drives,” in 2009 IEEE Energy Conversion Congress and Exposition, pp. 2466–2473, 2008.
[17] F. Chen and M. Dunnigan, “Comparative study of a sliding-mode observer and kalman filters for full state estimation in an induction machine,” IEE Proceedings-Electric Power Applications, vol. 149, no. 1, pp. 53–64, 2002.
[18] M. Khafallah, A. El Afia, A. Cheriti, F. El Mariami, A. Saad, and B. El Moussaoui, “Ekf based speed sensorless vector control of an induction machine,” in 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT’04., vol. 3, pp. 1338–1344, 2002.
[19] K. V. Shivaramakrishna, A. K. Chauhan, M. Raghuram, and S. K. Singh, “Sensorless control of induction motor using ekf: Analysis of parameter variation on ekf performance,” in 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1–4, 2002.
[20] Y. Zahraoui, C. Fahassa, M. Akherraz, and A. Bennassar, “Sensorless vector control of induction motor using an ekf and svpwm algorithm,” in 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 588–593, 2002.
[21] M. Khafallah, A. E. Afia, A. Cheriti, F. E. Mariami, A. Saad, and B. E. Moussaoui, “Ekf based speed sensorless vector control of an induction machine,” in Industrial Technology, 2004. IEEE ICIT ’04. 2004 IEEE International Conference on, pp. 1338–1344,2002.
[22] 王衍凱, 「以擴展型卡爾曼濾波器為基礎之感應馬達無感測器控制及定子與轉子阻抗估測」. 國立台灣科技大學機械工程學系，碩士論文, 2010.
[23] K. L. Shi, T. F. Chan, Y. K. Wong, and S. L. Ho, “Speed estimation of an induction motor drive using an optimized extended kalman filter,” IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 124–133, 2002.
[24] B. W. Williams and T. C. Green, “Steady-state control of an induction motor by estimation of stator flux magnitude,” IEE Proceedings B - Electric Power Applications, vol. 138, no. 2, pp. 69–74, 1991.
[25] M. Menaa, O. Touhami, and R. Ibtiouen, “Estimation of rotor resistance of an induction motor using extended kalman filter and spiral vector theory,” in Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003., vol. 2, pp. 1262–1266 vol.2, 2003.
[26] M. Barut, O. S. Bogosyan, and M. Gokasan, “Ekf based estimation for direct vector control of induction motors,” in IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, vol. 2, pp. 1710–1715 vol.2, 2002.
[27] In-Joong Ha and Sang-Hoon Lee, “An online identification method for both stator-and rotor resistances of induction motors without rotational transducers,” IEEE Transactions on Industrial Electronics, vol. 47, no. 4, pp. 842–853, 2000.
[28] M. Barut, S. Bogosyan, and M. Gokasan, “Switching ekf technique for rotor and stator resistance estimation in speed sensorless control of ims,” Energy Conversion and Management, vol. 48, no. 12, pp. 3120–3134, 2007.

[29] U. Syamkumar and B. Jayanand, “A reduced order smoothing filter for speed estimation of three phase induction motor,” IEEE Transactions on Power Electronics, pp. 1749–1754, 2017.
[30] R. Gunabalan, V. Subbiah, and B. R. Reddy, “Sensorless control of induction motor with extended kalman filter on tms320f2812 processor,” International Journal of Recent Trends in Engineering, vol. 2, no. 5, p. 14, 2009.
[31] Z. Wei and J. J. Luo, “Speed and rotor flux estimation of induction motors based on extended kalman filter,” IEEE Transactions on Power Electronics, pp. 157–160, 2010.
[32] J. Al-Tayie and P. Acarnley, “Estimation of speed, stator temperature and rotor temperature in cage induction motor drive using the extended kalman filter algorithm,” IEEE Proceedings-Electric Power Applications, vol. 144, no. 5, pp. 301–309, 1997.
[33] A. Roscoe, S. Blair, and G. Burt, “Benchmarking and optimisation of simulink code using real-time workshop and embedded coder for inverter and microgrid control applications,” in IEEE, pp. 1 – 5, 10 2009.
[34] 蕭昱鼎, 以擴展型卡爾曼濾波器為基礎進行感應馬達轉速, 負載, 轉子即定子溫度之即時估測. 國立台灣科技大學機械工程學系，碩士論文, 2019.
[35] Tony, “Tms320f28335 delfino 控制卡.” http:// www.mcudsp.com.tw/ C2000/F28335CC.html, 2019.
[36] R. Beguenane and M. E. H. Benbouzid, “Induction motors thermal monitoring by means of rotor resistance identification,” IEEE Transactions on Energy conversion, vol. 14, no. 3, pp. 566–570, 1999.
[37] K. Ogata, Discrete time control systems. Prentice Hall, Inc., 1987.
[38] 顏銘辰, 「整合熱電耦合及失效模式之感應馬達動態模型」. 國立台灣科技大學機械工程學系，碩士論文, 2015.