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Speed Control of a Separately Excited DC Motor Using New Proposed Fuzzy Neural Algorithm Based on FOPID Controller

  • Gholamreza FarahaniEmail author
  • Karim Rahmani
Article
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Abstract

The main goal of this paper is to control the speed of a separately excited DC motor (SEDM) with a new proposed fuzzy neural (FN) controller. This proposed method is used to adjust the fractional order proportional integral derivative (FOPID) parameters of the controller. Also the proposed control diagram solves the problem of parameter setting of the FN controller more effectively with use of particle swarm optimization (PSO) algorithm. In simulation with MATLAB 2017b, 250 series of data were used: 175 series of data, equivalent to 70% for training the designed neural network, and about 75 series, equivalent to 30% used to test and validate the neural network. The results show that the proposed method has a lower rise time and settling time for controlling the speed of SEDM in comparison with other methods such as Ziegler–Nichols, Cohen–Coon, PSO, genetic algorithm, artificial bee colony, artificial neural network, fuzzy logic controller and adaptive neuro-fuzzy interference system for PID and FOPID controllers.

Keywords

Fuzzy neural Particle swarm optimization Adaptive neuro-fuzzy interference system controller Fractional order PID Separately excited DC motor Speed control 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that there is no conflict of interest.

References

  1. Abed, W. N. A. (2015). Speed control of DC motor using adaptive neuro fuzzy controller. Journal of Scientific and Engineering Research, 2(1), 16–21.Google Scholar
  2. Adam Mohammed, O. O., & Taifor Ali, A. (2014). Comparative study of PID and fuzzy controllers for speed control of DC motor. International Journal of Innovative Research in Science, Engineering and Technology, 3(9), 16104–16110.Google Scholar
  3. Adewuyi, P. A. (2013). DC motor speed control: A case between PID controller and fuzzy logic controller. International Journal of Multidisciplinary Sciences and Engineering, 4(4), 36–40.Google Scholar
  4. Aggrawal, A., Mishra, A. K., & Zeeshan, A. (2014). Speed control of DC motor using particle swarm optimization technique by PSO tunned PID and FOPID. International Journal of Engineering Trends and Technology (IJETT), 16(2), 72–79.Google Scholar
  5. Akbari-Hasanjani, R., Javadi, S., & Sabbaghi-Nadooshan, R. (2014). DC motor speed control by self-tuning fuzzy PID algorithm. Transactions of the Institute of Measurement and Control, 37(2), 164–176.Google Scholar
  6. Alhanjouri, M. A. (2017). Speed control of DC motor using artificial neural network. International Journal of Science and Research (IJSR), 7(3), 2140–2148.Google Scholar
  7. Anguluri, R., Das, S., & Abraham, A. (2013). Fractional order PID controller design for speed control of chopper fed DC motor drive using artificial bee colony algorithm. In: Proceeding of IEEE world congress on nature and biologically inspired computing (NaBIC) (pp. 259–266), Fargo, North Dakota, USA, 12–14 Aug.Google Scholar
  8. Awouda, A. E. A., & Mergani, M. H. (2017). Design of self tuning PID controller using fuzzy logic for DC motor speed. International Journal of Social Science and Technology, 2(4), 27–33.Google Scholar
  9. Bansal, U. K., & Narvey, R. (2013). Speed control of DC motor using fuzzy PID controller. Advance in Electronic and Electric Engineering, 3(9), 1209–1220.Google Scholar
  10. Baruch, I. S., Garrido, R., Flores, J. M., & Martinez, J.-C. (2001). An adaptive neural control of a DC motor. In: Proceeding of IEEE International symposium on intelligent control (pp. 121–126), Mexico City, Mexico, 5–7 Sept.Google Scholar
  11. Carpinteri, A., & Mainardi, F. (1997). Fractals and fractional calculus in continuum mechanics (pp. 223–276). Wien: Springer.zbMATHGoogle Scholar
  12. Chaudhary, H., Khatoon, S., & Singh, R. (2016). ANFIS based speed control of DC motor. In Proceeding second international innovative applications of computational intelligence on power, energy and controls with their impact on humanity (CIPECH) (pp. 63–67), Ghaziabad, India, 18–19 Nov.Google Scholar
  13. Deraz, S. A. (2014). Genetic tuned PID controller based speed control of DC motor drive. International Journal of Engineering Trends and Technology (IJETT), 17(2), 88–93.Google Scholar
  14. Dobra, P., Trusca, M., & Lazea, G. (2002). Robust controller for a brushless DC motor based on the gain and phase margin. In: Proceeding of IEEE international workshop on advanced motion control (AMC) (pp. 197–202), Maribor, Slovenia, 3–5 July.Google Scholar
  15. Hashmia, A. L., & Dakheel, S. H. (2012). Speed control of separately excited DC motor using artificial neural network. Journal of Engineering and Development, 16(4), 349–362.Google Scholar
  16. Hassan, A. K., Saraya, M. S., Elksasy, M. S., & Areed, F. F. (2018). Brushless DC motor speed control using PID controller, fuzzy controller, and neuro fuzzy controller. International Journal of Computer Applications, 180(30), 47–52.Google Scholar
  17. Hidayat, R., Pramonohadi, S., Sarjiya, S., & Suharyanto, S. (2013). A Comparative study of PID, ANFIS and hybrid PID-ANFIS controllers for speed control of brushless DC motor drive. In: Proceeding of IEEE international conference on computer, control, informatics and its applications (pp. 117–122), Jakarta, Indonesia, 19–21 Nov.Google Scholar
  18. Hidayat, R., Pramonohadi, S., Sarjiya, S., & Suharyanto, S. (2015). The design of the hybrid PID-ANFIS controller for speed control of brushless DC motor. Journal of Theoretical and Applied Information Technology, 71(3), 367–375.Google Scholar
  19. Ho, M. T., Datta, A., & Bhattacharyya, S. P. (1997). A new approach to feedback stabilization: the discrete-time case. In: Proceedings of the 36th IEEE conference on decision and control (pp. 908–914), San Diego, CA, USA, 10–12 Dec.Google Scholar
  20. Hu, J., Burk, T. & Dawson, D. (1994). Nonlinear tracking controllers for brushless DC motors. In: Conference on industry applications society annual meeting (IAS) (pp. 480–487), Denver, CO, USA, 2–5 Oct.Google Scholar
  21. Ibrahim, H. E. A., Hassan, F. N., & Shomer, A. O. (2014). Optimal PID control of a brushless DC motor using PSO and BF techniques. Ain Shams Engineering Journal, 5(2), 391–398.Google Scholar
  22. Jagan Kumar, M., & Aadaleesan, P. (2015). Speed control of DC motor using genetic algorithm. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 6(4), 1239–1249.Google Scholar
  23. Jaiswal, M., & Phadnis, M. (2013). Speed control of DC motor using genetic algorithm based PID controller. International Journal of Advanced Research in Computer Science and Software Engineering, 3(7), 247–253.Google Scholar
  24. Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 697.Google Scholar
  25. Kavathe, R., Chandle, J. O., Patil, N., & Kokare, M. (2018). ANFIS Based Speed Control of BLDC Motor with Bidirectional DC–DC Converter. International Journal of Research and Scientific Innovation (IJRSI), 5(6), 153–158.Google Scholar
  26. Khandani, K., & Jalali, A. A. (2012). Robust fractional order control of a DC motor based on particle swarm optimization. Advanced Materials Research, 403, 5030–5037.Google Scholar
  27. Kraues, P. C. (1987). Analysis of electric machinery. Singapore: Mc Grow-Hill.Google Scholar
  28. Liu, Z. Z., Luo, F. L. & Rashid, M. H. (1999). Non-linear speed controllers for series DC Motor. In: Proceedings of IEEE international conference on power electronics and drive systems (PEDS 99) (vol. 1, pp. 333–338), Hong Kong, Hong Kong, 27–29 Jul.Google Scholar
  29. Mohammed, N. Q. (2017). DC motor drive with P, PI, and particle swarm optimization speed controllers. International Journal of Computer Applications, 166(12), 42–45.Google Scholar
  30. Montiel, O., Sepulveda, R., Melin, P., Castillo, O., Porta, M. A., & Meza, I. M. (2007). Performance of a simple tuned fuzzy controller and a PID controller on a DC motor. In: Proceeding of IEEE symposium on foundations of computational intelligence (FOCI) (531–537), Honolulu, HI, USA, 1–5 Apr.Google Scholar
  31. Palma, L. B., Coito, F. V. & Ferreira, B. G. (2015). PSO based on-line optimization for DC motor speed control. In: Proceedings of 9th international conference on compatibility and power electronics (CPE) (pp. 301–306), Costa da Caparica, Portugal, 24–26 Jun.Google Scholar
  32. Pan, I., & Das, S. (2013a). Enhancement of fuzzy PID controller with fractional calculus, intelligent fractional order systems and control (pp. 159–193). Berlin: Springer.Google Scholar
  33. Pan, I., & Das, S. (2013b). Intelligent fractional order systems and control: An introduction. Berlin: Springer.zbMATHGoogle Scholar
  34. Patel, A., & Parikh, K. (2014). Speed control of DC motor using PSO tuned PI controller. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 9(2), 4–8.Google Scholar
  35. Petrova, S. S., & Solov’ev, A. D. (1997). The origin of the method of steepest descent. Historia Mathematica, 24, 361–375.MathSciNetzbMATHGoogle Scholar
  36. Podlubny, I. (1999). Fractional-order systems and PIλ Dδ controllers. IEEE Transaction on Automatic Control, 44(1), 208–213.MathSciNetzbMATHGoogle Scholar
  37. Rajasekhar, A., Abraham, A., & Pant, M. (2011). Levy mutated artificial bee colony algorithm for global optimization. In: Proceeding of IEEE international conference on systems, man and cybernetics (SMC) (pp. 655–662), Anchorage, USA, 9–12 Oct.Google Scholar
  38. Tripura, P., & Srinivasa Kishore Babu, Y. (2014). Intelligent speed control of DC motor using ANFIS. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology archive, 26(1), 223–227.Google Scholar
  39. Varshney, A., Gupta, D., & Dwivedi, B. (2017). Speed response of brushless DC motor using fuzzy PID controller under varying load condition. Journal of Electrical Systems and Information Technology, 4, 310–321.Google Scholar
  40. Walaa, M. E., Naglaa, K. B., El-Sayed, M. I., & Moustafa Hassan, M. A. (2017). Speed control of DC motor using PID controller based on different techniques of PSO. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 6(12), 8914–8933.Google Scholar
  41. Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the ASME, 64, 759–768.Google Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Electrical Engineering and Information Technology InstituteIranian Research Organization for Science and Technology (IROST)TehranIran

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