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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 189))

Abstract

The paper deals with the applications of artificial neural networks in the control of the DC drive. In the paper three control structures are discussed. The first control structure uses a conventional PI controller. The second structure uses a neural network predictive control. The last structure is a sensorless control of the DC drive using feedforward neural network. The DC drives were simulated in program Matlab with Simulink toolbox. The main goal was to find the simplest neural network structures with minimum number of neurons, but simultaneously good control characteristics are required. Despite used neural networks, which are very simple, it was achieved satisfactory results.

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References

  1. Norgaard, M.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)

    Book  Google Scholar 

  2. Norgaard, M.: Neural network based control system design toolkit. Technical University of Denmark (2000)

    Google Scholar 

  3. Brandstetter, P.: A.C. Controlled Drives - Modern Control Methods. VSB-Technical University of Ostrava (1999)

    Google Scholar 

  4. Abachizadeh, M., Yazdi, M.R.H., Yousefi-Koma, A.: Optimal Tuning of PID Controllers Using Artificial Bee Colony Algorithm. In: Conference Proceedings of the International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, pp. 379–384 (2010)

    Google Scholar 

  5. Amamra, S.A., Barazane, L., Boucherit, M.S.: A New Approach of the Vector Control of the Induction Motor Using an Inverse Fuzzy Model. International Review of Electrical Engineering - IREE 3(2), 361–370 (2008)

    Google Scholar 

  6. Perdukova, D., Fedor, P.: Fuzzy Model Based Control of dynamic System. JEE-Journal of Electrical Engineering 7(3) (2007)

    Google Scholar 

  7. Luger, G.F.: Artificial Intelligence, Structures and Strategies for Complex Problem Solving. Williams (2003)

    Google Scholar 

  8. Russel, S.J., Norvig, P.: Artificial Intelligence, A Modern Approach. Prentice Hall (2006)

    Google Scholar 

  9. Vas, P.: Artificial-Intelligence-Based Electrical Machines and Drives. Oxford Science Publication (1999)

    Google Scholar 

  10. Haykin, S.: Neural Network a Comprehensive Foundation. Prentice-Hall, New Jersey (1999)

    Google Scholar 

  11. Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company (1996)

    Google Scholar 

  12. Levine, W.S.: The Control Handbook. CRC Press, Boca Raton (1996)

    MATH  Google Scholar 

  13. Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network ToolboxTM, User’s Guide. The MathWorks, Inc. (2012)

    Google Scholar 

  14. Holtz, J.: Sensorless Control of Induction Motor Drives. Proceedings of the IEEE 90(8), 1359–1394 (2002)

    Article  Google Scholar 

  15. Girovsky, P.J., Timko, J., Zilkova, J., Fedak, J.V.: Neural estimators for shaft sensorless FOC control of induction motor. In: Conference Proceedings, 14th International Power Electronics and Motion Control Conference, pp. T7-1–T7-5 (2010)

    Google Scholar 

  16. Gacho, J., Zalman, M.: IM Based Speed Servodrive with Luenberger Observer. Journal of Electrical Engineering 61(3), 149–156 (2010)

    Article  Google Scholar 

  17. Vas, P.: Sensorless Vector and Direct Torque Control. Oxford University Press, New York (1998)

    Google Scholar 

  18. Lascu, C., Boldea, I., Blaabjerg, F.: Comparative Study of Adaptive and Inherently Sensorless Observers for Variable-Speed Induction-Motor Drives. IEEE Transactions on Industrial Electronics 53(1), 57–65 (2016)

    Article  Google Scholar 

  19. Gadoue, S.M., Giaouris, D., Finch, J.W.: Sensorless Control of Induction Motor Drives at Very Low and Zero Speeds Using Neural Network Flux Observers. IEEE Transactions on Industrial Electronics 56(8) (2009)

    Google Scholar 

  20. Gallegos, M., Alvarez, R., Nunez, C., Cardenas, V.: Effects of Bad Currents and Voltages Acquisition on Speed Estimation for Sensorless Drives. In: Conference Proceedings Electron., Robot. Automotive Mech. Conference, pp. 215–219 (2006)

    Google Scholar 

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Correspondence to Pavel Brandstetter .

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Brandstetter, P., Bilek, P. (2013). Applications of Artificial Neural Networks in Control of DC Drive. In: Herrero, Á., et al. International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions. Advances in Intelligent Systems and Computing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33018-6_36

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  • DOI: https://doi.org/10.1007/978-3-642-33018-6_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33017-9

  • Online ISBN: 978-3-642-33018-6

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