Abstract
On the basis of Artificial Neural Network (ANN) theory, this paper has put forth a new kind of controller for Space Vector Modulated (SVM) Direct Torque Control (DTC) system for Induction Motor. The controller has features like smooth operation, high dynamics, stable and robust performance. The training algorithm used is Resilient Back Propagation (RBP). The paper also presents the comparison of proposed ANN controller with other intelligent controllers (Fuzzy based control and Fuzzy_Sliding Mode Control) for the drive, based on various control performance criterion at transient state as well as steady state. It is observed that while working with comparatively less control efforts, proposed ANN improves the performance of SVM_DTC in all-round way. Simulation results confirm the superiority and feasibility of the proposed ANN controller.
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
Preview
Unable to display preview. Download preview PDF.
References
Takahashi, Noguchi, T.: A new quick response and high efficiency control strategy of an induction motor. IEEE Transactions on Industrial Applications IA-22(5), 820–827 (1986)
Zhu, P., Kang, Y., Chen, J.: Improved Direct Torque Control Performance of Induction Motor with Duty Ratio Modulation. In: IEEE International Electric Machines and Drives Conference, vol. 2, pp. 994–998 (2003)
Lee, S.-B., Song, J.-H., Choy, L., Yoo, l.-Y.: Torque ripple reduction in DTC of induction motor driven by three-level inverter with low switching frequency. IEEE Transactions on Power Electronics 17(2), 255–264 (2002)
Beerten, J., Verveckken, J., Driesen, J.: Predictive Direct Torque Control for Flux and Torque Ripple Reduction. IEEE Transactions on Industrial Electronics 57(1), 404–412 (2010)
Habetler, T.G., Profumo, F., Pastorelli, M., Tolbert, L.M.: Direct torque control of induction motor using space vector modulation. IEEE Transactions on Industrial Electronics 28(5), 1045–1053 (1992)
Harashima, F.: Power Electronics and Motion Control - A Future Perspective. Proceedings of the IEEE 82(8), 1107–1111 (1994)
Awwad, A., Abu-Rub, H., Toliyat, H.: Nonlinear Autoregressive Moving Average (Narma- L2) Controller for Advanced AC Motor Control. In: 34rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2008, Orlando, Florida (2008)
Bose, B.K.: Artificial Neural Network Applications in Power Electronics. IEEE Transactions, 1631–1638 (2001)
Demuth, H., Beale, M., Hagan, M.: Neural network toolbox user’s guide for use with MATLAB. The Math Works, Inc., Natick (2006)
Abbou, M., Akherraz, A.: Real-time DSP implementation of DTC neural network-based control for induction motor drive. In: 5th IET International Conference on Power Electronics, Machines and Drives, vol. 6, pp. 1–5 (2010)
Narendra, K.S., Mukhopadhyay, S.: Adaptive Control Using Neural Networks and Approximate Models. IEEE Transactions on Neural Networks 8, 475–485 (1997)
Riedmiller, M., Braun, H.: A direct adaptive method for faster back propagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, vol. 1, pp. 586–591 (1993)
Lascu, C., Boldea, I., Blaabjerg, F.: A Modified Direct Torque Control for Induction Motor Sensorless Drive. IEEE Tran. on Ind. Appl. 36(1), 122–130 (2000)
Vas, P.: Artificial-Intelligent-Based Electrical Machines and Drives. In: Application of Fuzzy, Neural, Fuzzy-Neural and Genetic-Algorithm-Based Techniques. Oxford Univ. Press, Oxford (1999)
Fodor, D., Ionescu, F., Floricau, D., Six, J.P., Delarue, P., Diana, D.: Neural Networks Applied for Induction Motor Speed Sensorless Estimation, vol. 1, pp. 181–186 (1995)
Luis, I.H., Cabrera, A., Elbuluk, M.E.: Tuning the stator resistance of induction motors using artificial neural network. IEEE International on Power Electronics 12(5), 779–787 (1997)
Kola, S., Varatharas, L.: Identifying 3 phase I.M faults using neural networks. ISA Transactions (39), 433–439 (2000)
Tag Eldin, E.M., Emara, H.R., Aboul-Zahab, E.M., Refaat, S.S.: Monitoring and Diagnosis of External faults in Three Phase Induction Motor using Artificial Neural Network. In: IEEE Power Engineering Society General Meeting (2007)
Lascu, C., Boldea, Blaabjerg, F.: A modified direct torque control for induction motor sensorless drive. IEEE Transactions on Industry Applications 36, 122–130 (2000)
Ha, Q.P., Nguyen, Q.H., Rye, D.C., Durrant-Whyte, H.F.: Fuzzy sliding mode controllers with applications. IEEE Trans. on Ind. Electron. 48(1), 38–46 (2001)
Agamy, M.S., Yousef, H.A., Sebakhy, O.A.: Adaptive Fuzzy Variable Structure Control of Induction Motors. In: IEEE Canadian Conference on Electrical and Computer Engineering, pp. 89–94 (2004)
Jadhav, S.V., Chaudhari, B.N., Kumar, K.: Direct Torque Control of Induction Motor using Artificial Neural Network. In: PEDES 2012 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jadhav, S.V., Chaudhari, B.N. (2013). A Novel Artificial Neural Network Based Space Vector Modulated DTC and Its Comparison with Other Artificial Intelligence (AI) Control Techniques. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)