Controle De Velocidade De Baixo Custo Para Motores De Indução Sem Sensores Mecânicos
DOI:
https://doi.org/10.18618/REP.2007.3.233243Keywords:
Filtro de Kalman, Motor de Indução, sensores de baixo custo, sensorless, servo de velocidadeAbstract
Este artigo propõe um controle direto orientado pelo campo para um servo de velocidade sensorless usando motor de indução trifásico. A principal motivação deste artigo é a substituição dos elementos de medida mais onerosos, encoder e sensores de efeito Hall, por sistemas de baixo custo. Isto é possível utilizando-se um algoritmo sensorless, sensores shunt de baixo custo e filtro de Kalman. O filtro de Kalman fornece estimativas de corrente que são utilizadas em um algoritmo do tipo mínimos quadrados recursivo (RLS-Recursive Least- Square) para estimar a velocidade do rotor. Conforme descrito neste artigo, é possível trabalhar com ambas as técnicas e obter uma boa estimação de velocidade com baixo nível de ruído e pequeno esforço computacional. Resultados experimentais são apresentados para validar o trabalho proposto.
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A. V. Leite, R. E. Araújo, and D. Freitas, "Full and reduced order extended Kalman filter for speed estimation in induction motor drives:A comparative study," in Proceedings, 32th Power Electronics Specialists Conference. Aachen: IEEE, 2004. https://doi.org/10.1109/PESC.2004.1355479 DOI: https://doi.org/10.1109/PESC.2004.1355479
J. L. Mora, A. Torralda, and L. G. Franquelo, "An adaptive speed estimator for induction motors based on a Kalman filter with low sample time," in Proceedings, 32nd Power Electronics Specialists Conference-PESC'01. Vancouver: IEEE, 2001. https://doi.org/10.1109/PESC.2001.954216 DOI: https://doi.org/10.1109/PESC.2001.954216
L.Salvatore, S. Stasi, and F. Cupetino, "Improved rotor speed estimation using two Kalman filter-based algorithms," in Proceedings, 32nd Annual Meeting of the IEEE Industry Applications Society-IAS'01. Chicago: IEEE, 2001. https://doi.org/10.1109/IAS.2001.955402 DOI: https://doi.org/10.1109/IAS.2001.955402
C. Schauder, "Adaptive speed identification for vector control of induction motors without rotational transducers," IEEE Transactions on Industry Applications, vol. 28, no. 5, pp. 1054-1061, Sep. 1992. https://doi.org/10.1109/28.158829 DOI: https://doi.org/10.1109/28.158829
L. Zhen and L. Xu, "Sensorless field orientation control of induction machines based on mutual mras scheme," IEEE Transactions on Industrial Electronics, vol. 45, no. 3, pp. 824-831, Oct.1998. https://doi.org/10.1109/41.720340 DOI: https://doi.org/10.1109/41.720340
C. Lascu, I. Boldea, and F. Blaabjerg, "A modified direct torque control for induction motor sensorless drive," IEEE Transactions on Industrial Electronics, vol. 36, no. 3, pp. 122-130, Feb. 2000. https://doi.org/10.1109/28.821806 DOI: https://doi.org/10.1109/28.821806
C. B. Jacobina, A. M. N. Lima, and F. Salvatori, "Flux and torque control of an induction machine using linear discrete state space approach," in Proceedings, 23rd International Conference on Industrial Electronics, Control, And Instrumentation IECON'97. New Orleans: IEEE, 1997. https://doi.org/10.1109/IECON.1997.671786 DOI: https://doi.org/10.1109/IECON.1997.671786
M. Vélez-Reyes, K. Minami, and G. G. Verguese, Recursive speed and parameter estimation for induction machines," in Conference Record, 1989IEEE Industry Applications Society Annual Meeting. San Diego: IEEE, 1989. https://doi.org/10.1109/IAS.1989.96712 DOI: https://doi.org/10.1109/IAS.1989.96712
K. Minami, M. Vélez-Reyes, and G. G. Verguese, "Multi-stage speed and parameter estimation for induction machines," in Conference Record, 22nd Annual IEEE Power Electronics Specialists Conference-PESC'91. Cambridge: IEEE, 1991. https://doi.org/10.1109/PESC.1991.162736 DOI: https://doi.org/10.1109/PESC.1991.162736
H. T. Câmara and H. A. Gründling, "A MRLS with MRC applied to sensorless speed control induction motor drive," in Conference Record, VI Induscon. Joinville-SC: IEEE, 2004.
K. L. Shi, T. F. Chan, Y. K. Wong, and S. L. Ho, "Speed estimation of an induction motor drive usingan optimized extended Kalman filter," IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 124-133, Feb. 2002. https://doi.org/10.1109/41.982256 DOI: https://doi.org/10.1109/41.982256
M. Boutayeb, H. Rafaralahy, and M. Darouach, "Convergence analysis of the extended Kalman filter used as an observer for nonlinear deterministic discrete-time systems," IEEE Transactions on Automatic Control, vol. 42, no. 4, pp. 581-586, Apr. 1997. https://doi.org/10.1109/9.566674 DOI: https://doi.org/10.1109/9.566674
R. E. Kalman , "A new approach to linear filtering and prediction problems," Journal of Basic Engineering, vol. Series 82D, pp. 35-45, Mar. 1960. https://doi.org/10.1115/1.3662552 DOI: https://doi.org/10.1115/1.3662552
B. Carew and P. R. Bélanger, "Identification of optimum steady-state gain for systems with unknow noise covariances," IEEE Transactions on Automatic Control, vol. AC-18, no. 6, pp. 582-587, Dec. 1973. https://doi.org/10.1109/TAC.1973.1100420 DOI: https://doi.org/10.1109/TAC.1973.1100420
A. H. Jazwinski, "Adaptive filtering," Automatica, vol. 5, pp. 475-485, Jul. 1969. https://doi.org/10.1016/0005-1098(69)90109-5 DOI: https://doi.org/10.1016/0005-1098(69)90109-5
R. Mehra, "On the identification of variances and adaptive Kalman filtering," IEEE Transactions on Automatic Control, vol. AC-15, no. 2, pp. 175-184, Apr. 1970. https://doi.org/10.1109/TAC.1970.1099422
R. K. Mehra, "Approaches to adaptive filtering," IEEE Transactions on Automatic Control, vol. 17, no. 5, pp. 693-698, Oct. 1972. https://doi.org/10.1109/TAC.1972.1100100 DOI: https://doi.org/10.1109/TAC.1972.1100100
R. Cardoso, E. M. Hemerly, H. T. Câmara, and H. A. Gründling, "Identification procedure for Kalman filter tuning," in Conference Record, VI Induscon. Joinville-SC: IEEE, 2004.
J. Jung and Kwanghee, "A dynamic decoupling control scheme for high-speed operation of induction motors," IEEE Transactions on Industrial Electronics, vol. 46, no. 1, pp. 100-110, Feb.1999. https://doi.org/10.1109/41.744397 DOI: https://doi.org/10.1109/41.744397
T. Kailath, "An innovations approach to least-squares estimation, part i: Linear filtering in aditive white noise," IEEE Transactions on Automatic Control, vol. AC-13, no. 6, pp. 646-655, Dec. 1968. https://doi.org/10.1109/TAC.1968.1099025 DOI: https://doi.org/10.1109/TAC.1968.1099025
R. Mehra, "On the identification of variances and adaptive Kalman filtering," IEEE Transactions on Automatic Control, vol. AC-15, no. 2, pp. 175-184, Apr. 1970. https://doi.org/10.1109/TAC.1970.1099422 DOI: https://doi.org/10.1109/TAC.1970.1099422
Y.D. Landau, Adaptive Control: The Model Reference Approach. New York: Marcel Dekker Inc., 1979. https://doi.org/10.1109/TSMC.1984.6313284 DOI: https://doi.org/10.1109/TSMC.1984.6313284
F. C. Schweppe, "Evaluation of likelihood functionsfor gaussian signals," IEEE Transactions on Information Theory, vol. IT-11, pp. 61-70, Jan. 1965. https://doi.org/10.1109/TIT.1965.1053737 DOI: https://doi.org/10.1109/TIT.1965.1053737
R. Cardoso, E. M. Hemerly, H. T. Câmara, and H. A. Gründling, "Impact of Correlation Errors on the Optimum Kalman Filter Gain Identification in a Single Sensor Environment," in Conference Record, 39th IAS Annual Meeting. Seattle: IEEE, 2004. https://doi.org/10.1109/IAS.2004.1348666 DOI: https://doi.org/10.1109/IAS.2004.1348666
R. Cardoso, E. M. Hemerly, H. T. Câmara, and H. A. Gründling, "Impact of Correlation Errors on OptimumKalman Filter Matrices Gains Identification in Multicoordinate Systems," in Conference Record,ACC 2005 - American Control Conference. Portland: IEEE, 2005. https://doi.org/10.1109/ACC.2005.1470724 DOI: https://doi.org/10.1109/ACC.2005.1470724
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