Abstract
Direct torque control (DTC) based on artificial neural network (ANN) of a five-phase permanent magnet synchronous motor drive (PMSM) is presented in this paper. Using the mathematical model of the five-phase motor, DTC control strategy is developed, and the corresponding controllers are properly designed in order to provide independent torque and flux control. In order to improve the performance of the DTC, a neural network based DTC scheme is adopted instead of the DTC based on the look-up table. The employed neural network uses the Levenberg-Marquardt back propagation algorithm for the adjustment of weights to increase the learning process accuracy. The efficacy of the proposed method is verified by simulation for various dynamic operating conditions, and the system’s performance is compared with conventional DTC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guiti, M., Naitseghir, A.: Direct direct torque control with ANN hysteresis controller for PMSM. In: The 4th International Conference on Electrical Engineering (ICEE) 2015, Boumerdes, Algeria, 13–15 December, pp. 1–6 (2015)
El Idrissi, A.E.J., Zahid, N., Jedra, M.: Optimized DTC by genetic speed controller and inverter based neural networks SVM for PMSM. In: Second International Conference on Innovative Computing Technology (INTECH), Casablanca, 18–20 September, pp. 1–4 (2012)
Gulez, K., Adam, A.A.: Adaptive neural network based controller for direct torque control of PMSM with minimum torque ripples. In: SICE Annual Conference 2007, Takamatsu, Japan, 17–20 September, pp. 174–179 (2007)
Kim, N., Kim, M.: Modified direct torque control system of five phase induction motor. J. Electr. Eng. Technol. 4(2), 266–271 (2009)
Raj, L.R.L.V., Jidin, A., Che Wan Mohd Zalani, C.W.M.F., Abdul Karim, K., Wee Yen, G., Jopri, M.H.: Improved performance of DTC of five-phase induction machines. In: 2013 IEEE 7th International Power Engineering and Optimization Conference, Langkawi, Malaysia, 3–4 June, pp. 613–618 (2013)
Hosseyni, A., Trabelsi, R., Mimouni, M.F., Iqbal, A.: Vector controlled five-phase permanent magnet synchronous motor drive. In: 23rd International Symposium on Industrial Electronics (ISIE), Istanbul, pp. 2122–2127 (2014)
Sneessens, C., Labbe, T., Baudart, F., Matagne, E.: Position sensorless control for five-phase permanent-magnet synchronous motors. In: International Conference on Advanced Intelligent Mechatronics, Besançon, France, July 8–11, pp. 794–799 (2014)
Moghbeli, H., Zarei, M., Mirhoseini, S.S.: Transient and steady states analysis of traction motor drive with regenerative braking and using modified direct torque control. In: Proceedings of PEDSTC, pp. 615–620 (2015)
Jannati, M., Anbaran, S., Zaheri, D.M., Idris, N.R.N., Aziz, M.J.A.: A new speed sensorless SVM-DTC in induction motor by using EKF. In: 2013 IEEE Student Conference on Research and Development, Putrajaya, December 16–17, pp. 94–99 (2013)
Zolfaghari, M., Taher, S.A., Munuz, D.V.: Neural network-based sensorless direct power control of permanent magnet synchronous motor. Ain Shams Eng. J. 7(2), 729–740 (2017)
Bakouri, A., Mahmoudi, H., Abbou, A., Moutchou, M.: Optimizing the wind power capture by using DTC technique based on artificial neural network for a DFIG variable speed wind turbine. In: The 10th International Conference on Intelligent Systems: Theories and Applications (SITA), Rabat, Morocco, 20-21 October, pp. 1–7 (2015)
Barikand, S.K., Jaladi, K.K.: Five-phase induction motor DTC-SVM scheme with PI Controller and ANN controller. In: Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016), vol. 25, pp. 816–823 (2016)
Krishnaveni, D., Sivaprakasam, A., Manigandan, T.: Optimum voltage vector selection in direct torque controlled PMSM using intelligent controller. In: International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India, 6–8 March, pp. 1–6 (2014)
Gottapu, K., Kiran, U.S., Raju, U.S., Nagasai, P., Prasad, S., Rao, P.T.: Designand analysis of artificial neural network based controller for speed control of induction motor using DTC. Int. J. Eng. Res. Appl. 4(4), 259–264 (2014)
Bossoufi, B., Karim, M., Ionita, S., Lagrioui, A.: DTC control based artificial neural network for high performance PMSM drive. J. Theor. Appl. Inf. Technol. 33(2), 165–176 (2011)
Xue, S., Wen, X.: Simulation analysis of two novel multiphase SVPWM strategies. In: IEEE International Conference on Industrial Technology, ICIT 2005, Hong Kong, pp. 1337–1342 (2005)
Yu, F., Zhang, X., Qiao, M., Du, C.: The direct torque control of multiphase permanent magnet synchronous motor based on low harmonic space vector PWM. In: International Conference on Industrial Technology, pp. 1–5 (2008)
Jones, M., Dordevic, O., Bodo, N., Levi, E.: PWM algorithms for multilevel inverter supplied multiphase variable-speed drives. Electronics 16(1), 22–31 (2012)
Parsaand, L., Toliyat, H.A.: Sensorless direct torque control of five-phase interior permanent magnet motor drives. In: 39th IAS Annual Meeting on Industry Applications Conference 2004, 3–7 October, vol. 2, pp. 992– 999 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kamel, T., Abdelkader, D., Said, B., Iqbal, A. (2018). Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-73192-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73191-9
Online ISBN: 978-3-319-73192-6
eBook Packages: EngineeringEngineering (R0)