DESIGN OF AN OPTIMIZED ADAPTIVE PID CONTROLLER FOR INDUCTION MOTOR DRIVE

Sudeshna Ghosh, Harsh Goud, Pankaj Swarnkar, and Dinesh M. Deshpande

References

  1. [1] T.I. Haweel, Modeling induction motors, International Journal on Electrical Engineering and Informatics, 1(2), 2012, 361–370.
  2. [2] A. Sabir and S. Ibrir, Induction motor speed control using reduced order model, Automatika, 59(3–4), 2018, 274–285.
  3. [3] M. Vasudevan, R. Arumugam, and S. Paramasivam, High performance adaptive intelligent direct torque control schemes for induction motor drives, Serbian Journal of Electrical Engineering, 2(1), 2005, 93–116.
  4. [4] S. Ghosh, P. Swarnkar, and D.M. Deshpande, Control strategies governing induction motors as industrial drives – A technical review, International Journal on Emerging Technologies, 10(1), 2019, 97–105.
  5. [5] E.E. EL-Kholy, High performance induction motor drive based on adaptive variable structure control, Journal of Electrical Engineering, 56(3–4), 2005, 64–70.
  6. [6] S. Senthil Kumar and S. Vijayan, Simulation of high performance PID controller for induction motor speed control with mathematical modeling, Research Journal of Applied Sciences, Engineering and Technology, 18(6), 2013, 3343–3348.
  7. [7] H. Goud and P. Swarnkar, Signal synthesis model reference adaptive controller with artificial intelligent technique for a control of continuous stirred tank reactor, International Journal of Chemical Reactor Engineering, 17(2), 2018, 1–11.
  8. [8] S. Hussain, A. Mohammad, and M.A. Bazaz, Neural predictive observer for sensorless-controlled induction motor drive, Control and Intelligent Systems, 45(2), 2017, 84–91.
  9. [9] H. Goud and P. Swarnkar, Investigations on metaheuristic algorithm for designing adaptive PID controller for continuous stirred tank reactor, MAPAN-Journal of Metrology Society of India, 34(1), 2019, 113–119.
  10. [10] R. Sen, C. Pati, S. Dutta, and R. Sen, Comparison between three tuning methods of PID control for high precision positioning stage, MAPAN-Journal of Metrology Society of India, 30(1), 2015, 65–70.
  11. [11] K.S. Saji and M. Sakikumar, Tuning employing fuzzy and ANFIS for a pH process, Control and Intelligent Systems, 40(2), 2012, 95–101.
  12. [12] R.J. Guo, B. Cain, and J. Armstrong, Tuning fuzzy logic motor model for pilot control behaviour during helicopter flight manoeuvres, Control and Intelligent Systems, 46(3), 2018, 1–8.
  13. [13] S. Ghosh, P. Swarnkar, and D.M. Deshpande, Comparative analysis based on simulation & design aspects of three phase four switch inverter for industrial applications, International Journal of Mathematical, Engineering and Management Sciences, 4(6), 2019, 1325–1340.
  14. [14] H. Goud and P. Swarnkar, Analysis and simulation of the continuous stirred tank reactor system using genetic algorithm, Harmony search and nature inspired optimization algorithms (Singapore: Springer, 2019), 1141–1151.
  15. [15] A.O. Guimaraes, J.P. Silva, and E.R.M. Dantas, Genetic algorithm applied to control of DC motor with disturbance rejection by feedforward action, Control and Intelligent Systems, 43(1), 2015, 42–49.
  16. [16] W. Deng, H. Zhao, Y. Luo, and X. Li, A mixed optimization method based on artificial intelligence and its application, Control and Intelligent Systems, 43(4), 2015.
  17. [17] D. Karaboga and B. Akay, A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, 214(1), 2009, 108–132.
  18. [18] Z. Farnaz, H.S. Sajith, A.D. Abeysekara, et al., DC motor torque control using state estimation, Control and Intelligent Systems, 44(3), 2016, 130–138.
  19. [19] M.A. Hannan, A.A. Jamal, A. Mohamed, and A. Hussain, Optimization techniques to enhance the performance of induction motor drives: A review, Renewable and Sustainable Energy Reviews, 81(3), 2018, 1611–1626.
  20. [20] C. Zhou, X. Zhao, and Q. Yu, Adaptive robust control for active suspension system using T–S fuzzy model approach, Control and Intelligent Systems, 46(2), 2018, 46–54.

Important Links:

Go Back