Advertisement

Speed and Torque Control of Induction Motor Using Adaptive Neuro-Fuzzy Interference System with DTC

  • Ranjit Kumar BindalEmail author
  • Inderpreet Kaur
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

Every industry needs speed and torque ripple control of induction motor in large number of applications. The number of induction motor takes more time during starting, settling and transient period. As more time is taken by the motor so there are more losses, more heat, less efficiency and more ripples are produced. To overcome these drawback, direct torque control technique known as conventional technique, is used with induction motors, but with up to certain limits the drawbacks are reduced. In this paper a new technique an Adaptive Neuro-Fuzzy Interference System (ANFIS) with DTC is proposed to overcome the drawbacks of conventional DTC technique. Now by implementing and comparing the proposed technique ANFIS with conventional one it is seen that the system becomes less complicated, the performance of the speed and torque control of the induction motor is also improved. It is also seen that as we compared the proposed technique with conventional one the rise time is reduced by 256 ms settling time is reduced by 687 ms and transient time is reduced by 202 ms and torque ripples are also reduced and the overall performance of the induction motor is improved.

Keywords

Three-phase induction motor Adaptive Neuro-Fuzzy Interference System (ANFIS) direct torque control 

References

  1. 1.
    Sekhar, D.C., Marutheswar, G.V.: Direct torque control of three phase induction motor with ANFIS and CUCKOO search algorithms. Int. J. Pure Appl. Math. 114, 501–514 (2017)Google Scholar
  2. 2.
    Mishra, R.N., Mohanty, K.B.: Implementation of feedback-linearization-modelled induction motor Drive through an adaptive simplified neuro-fuzzy approach. Sadhana 42, 2113–2135 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Swain, S.D., Ray, P.K., Mohanty, K.B.: Improvement of power quality using a robust hybrid series active power filter. IEEE Trans. Power Electron. 32, 3490–3498 (2016)CrossRefGoogle Scholar
  4. 4.
    Mishra, R.N., Mohanty, K.B.: Real time implementation of an ANFIS-based induction motor drive via feedback linearization for performance enhancement. Eng. Sci. Technol. Int. J. 19, 1714–1730 (2016)CrossRefGoogle Scholar
  5. 5.
    Li, J.Q., Li, W.L., Deng, G., Ming, Z.: Continuous-behaviour and discrete-time combined control for linear induction motor-based urban rail transit. IEEE Trans. Magn. 52(7), 1–4 (2016)Google Scholar
  6. 6.
    Alexandridis, A., Chondrodima, E., Sarimveis, H.: Cooperative learning for radial basis function networks using particle swarm optimization. Appl. Soft Comput. 49, 485–497 (2016)CrossRefGoogle Scholar
  7. 7.
    Krishna, V., Mamanduru, R., Subramanian, N., Tiwari, M.K.: Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization. Comput. Ind. Eng. 96, 201–215 (2016)CrossRefGoogle Scholar
  8. 8.
    Venkataramana, N.N., Thankachan, J., Singh, S.P.: A neuro-fuzzy direct torque control using bus-clamped space vector modulation. IET Tech. Rev. 33, 205–217 (2016)CrossRefGoogle Scholar
  9. 9.
    Venkataramana, N.N., Singh, S.P.: A comparative analytical performance of F2DTC and PIDTC of induction motor using the space ds-1104. IEEE Trans. Ind. Electron. 62, 7350–7359 (2015)CrossRefGoogle Scholar
  10. 10.
    Ramesh, T., Panda, K.: Type-2 fuzzy logic control based MRAS speed estimator for speed sensor less direct torque and flux control of an induction motor drive. ISA Trans. 57, 262–275 (2015)CrossRefGoogle Scholar
  11. 11.
    Mishra, R.N., Mohanty, B.K.: Performance enhancement of a linearized induction motor drive using ANFIS based torque controller. In: Proceedings of the 12th India International Conference (INDICON), vol. 5, pp. 1–6 (2015)Google Scholar
  12. 12.
    Igoulalenei, I., Benyoucef, I., Tiwari, M.K.: Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem. Expert Syst. Appl. 42, 3342–3356 (2015)CrossRefGoogle Scholar
  13. 13.
    Uddin, M.N., Huang, Z.R.: Development and implementation of a simplified self-tuned neuro-fuzzy-based IM drive. IEEE Trans. Ind. Appl. 50, 51–59 (2014)CrossRefGoogle Scholar
  14. 14.
    Sekhar, D.C., Marutheshwar, G.V.: Modelling and field oriented control of induction motor by using an adaptive neuro fuzzy interference system control technique. Int. J. Ind. Electron. Electr. Eng. 2, 75–81 (2014)Google Scholar
  15. 15.
    Wang, S.Y., Tseng, C.L., Chiu, C.J.: Design of a novel adaptive TSK-fuzzy speed controller for use in direct torque control induction motor drives. Appl. Soft Comput. 31, 396–404 (2015)CrossRefGoogle Scholar
  16. 16.
    Lia, Y., Weib, H.: Research on controlling strategy of dual bridge matrix converter-direct torque control of induction motor. Energy Proc. 16, 1650–1658 (2012)CrossRefGoogle Scholar
  17. 17.
    Duanx, X., Deng, H., Li, H.: A saturation-based tuning method for fuzzy PID controller. IEEE Trans. Ind. Electron. 60, 577–585 (2013)Google Scholar
  18. 18.
    Kumar, G.D., Pathak, M.K.: Comparison of adaptive neuro-fuzzy based space vector modulation for two level inverter. Int. J. Electr. Power Energy Syst. 38, 9–19 (2012)CrossRefGoogle Scholar
  19. 19.
    Pimkumwonga, N., Onkronga, A., Sapaklomb, T.: Modelling and simulation of direct torque control induction motor drives via constant volt/hertz technique. Proc. Eng. 31, 1211–1216 (2012)CrossRefGoogle Scholar
  20. 20.
    Mohammed, T.L., Muthanna, J.M., Ahmed, I.S.: Space vector modulation direct torque speed control of induction motor. Proc. Comput. Sci. 5, 505–512 (2011)CrossRefGoogle Scholar
  21. 21.
    Geyer, T.: Computationally efficient model predictive direct torque control. IEEE Trans. Power Electron. 26, 2804–2816 (2011)CrossRefGoogle Scholar
  22. 22.
    Rubaai, A., Jerry, J., Smith, S.T.: Performance evaluation of fuzzy switching position system controller for automation and process industry control. IEEE Trans. Ind. Appl. 47, 2274–2282 (2011)CrossRefGoogle Scholar
  23. 23.
    Tang, Z.R., Bai, B., Xie, D.: Novel direct torque control based on space vector modulation with adaptive stator flux observer for induction motors. IEEE Trans. Magn. 46, 3133–3136 (2010)CrossRefGoogle Scholar
  24. 24.
    Areed, F.G., Haikal, A.Y., Mohammed, R.H.: Anadaptive neuro-fuzzy control of an induction motor. Ain Shams Eng. J. 1, 71–78 (2010)CrossRefGoogle Scholar
  25. 25.
    Uddin, M.N., Chy, M.I.: A novel fuzzy logic controller based torque and flux controls of a synchronous motor. IEEE Trans. Ind. Appl. 46, 1220–1229 (2010)CrossRefGoogle Scholar
  26. 26.
    Geyer, T., Papafotiou, G., Morari, M.: Model predictive direct torque control—part I, part-II: concept, algorithm, and analysis. IEEE Trans. Power Electron. 56(6), 1894–1905 (2009)Google Scholar
  27. 27.
    Karakas, E., Vardarbasi, S.: Speed control of motor by self-tuning fuzzy PI controller with artificial neural network. Sadhana 32, 587–596 (2007)CrossRefGoogle Scholar
  28. 28.
    Kouro, S., Rodriguez, J.: High-performance torque and flux control for multilevel inverter fed induction motors. IEEE Trans. Power Electron. 22(6), 2116–2123 (2007)CrossRefGoogle Scholar
  29. 29.
    Toufouti, R., Meziane, S., Benalla, H.: Direct torque control for induction motor using intelligent techniques. J. Theor. Appl. Inf. Technol. 3(3), 35–44 (2007)Google Scholar
  30. 30.
    Changyu, S., Lixia, W., Qian, I.: Optimization of Injection modeling process parameters using combination of artificial neural network and genetic algorithm method. J. Mater. Process. Technol. 183, 412–418 (2007)CrossRefGoogle Scholar
  31. 31.
    Lin, F.J., Huang, P.K., Chou, W.D.: Recurrent-fuzzy neural- network-controlled linear Induction motor servo drives using Genetic algorithms. IEEE Trans. Ind. Electron. 54, 449–1461 (2007)Google Scholar
  32. 32.
    Grabowski, P.Z., Bose, B.K., Blaabjerg, F.: A simple direct-torque neuro-fuzzy control of PWM-Inverter-fed induction motor drive. IEEE Trans. Ind. Electron. 47, 863–870 (2000)CrossRefGoogle Scholar
  33. 33.
    Bindal, R.K., Kaur, I.: Performance of three phase induction motor of direct torque control using fuzzy logic controller. Int. J. Pure Appl. Math. 118, 159–175 (2018)CrossRefGoogle Scholar
  34. 34.
    Bindal, R.K., Kaur, I.: Comparative analysis of different controlling techniques using direct torque control on induction motor. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 191–196. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentChandigarh UniversityGharuan, MohaliIndia

Personalised recommendations