Skip to main content
SpringerLink
Log in

Stability analysis and implementation of chopper fed DC series motor with hybrid PID-ANN controller

  • Regular Paper
  • Control Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

The attempt is made to enhance the performance of a closed loop control of DC series motor fed by DC chopper (DC-DC buck converter) by hybridization of PID controller with an intelligent control using ANN (Artificial Neural Network) controller. This system consists of inner current controller loop and outer PID-ANN based speed controller loop. The current controller allows the PWM (Pulse Width Modulation) signal when the motor current is less than set value. The PID-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady state and dynamic operating condition with various set speed and various load torque. The rise time, maximum over shoot, settling time, steady state error and speed drops are taken for comparison with conventional PID controller and existing work. The steady state stability analysis of the system also is made by using the transfer function model with MATLAB. The training data for PID-ANN controller is taken from conventional PID controller. The Hybrid PID-ANN controller with DC chopper has better control over the conventional PID controller and the reported existing work. This system is simulated using MATLAB/Simulink and also it is implemented with a NXP 80C51 family Microcontroller (P89V51RD2 BN) based Embedded System.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. M. Zurada, “Introduction to artificial neural systems,” Mumbai: Jaico Publishing House, 1992.

    Google Scholar 

  2. MATLAB, Neural Network Tool Box User’s Guide, Version 3, Massachusetts: The Mathworks Inc.

  3. S. N. Biswas, Industrial Electronics, Dhanpat Rai Publishing Company (P) Ltd., 1996.

    Google Scholar 

  4. N. Senthil Kumar, V. Sadasivam, H. M. Asan Sukriya, and S. Balakrishnan, “Design of low cost universal artificial neuron controller for chopper fed embedded DC drives,” Science Direct, Elsevier B.V., Applied Soft Computing, vol. 8, no. 4, pp. 1637–1642, September 2008.

    Article  Google Scholar 

  5. N. Senthil Kumar, V. Sadasivam, and H. M. Asan Sukriya, “A comparative study of PI, fuzzy, and ANN controllers for chopper-fed DC drive with embedded systems approach,” Electric Power Components and Systems, vol. 36, no. 7, pp. 680–695, 2008.

    Article  Google Scholar 

  6. M. Muruganandam, N. Senthil Kumar, and V. Sadasivam, “A low-cost four-quadrant chopper-fed embedded DC drive using fuzzy controller,” Inter National Journal of Electric Power Components and Systems, vol. 35, no. 8, pp. 907–920, August 2007.

    Article  Google Scholar 

  7. H. A. Yousef and H. M. Khalil, “A fuzzy logic-based control of series DC motor drives,” Proc. of the IEEE International Symposium, vol. 2, no. 10–14, pp. 517–522, July 1995.

    Google Scholar 

  8. M. Muruganandam and M. Madheswaran, “Performance analysis of fuzzy logic controller based DC-DC converter fed DC series motor,” Proc. of IEEE International Conference, Chinese Control and Decision Conference, pp. 1635–1640, June 2009.

    Google Scholar 

  9. M. Muruganandam and M. Madheswaran, “Modeling and simulation of modified fuzzy logic controller for various types of DC motor drives,” Proc. of IEEE International Conference on Control, Automation, Communication and Energy Conservation, June 2009.

    Google Scholar 

  10. K. B. Naik and S. D. Pandey, “Analysis and performance of a chopper fed DC series motor during steady-state and dynamic operating conditions,” Science Direct, Science Direct, Elsevier, Electric Power Systems Research, vol. 17, no. 2, pp. 139–147, September 1989.

    Article  Google Scholar 

  11. S.-M. Kim and W.-Y. Han, “Induction motor servo drive using robust PID-like neuro-fuzzy controller,” Control Engineering Practice, vol. 14, no. 5, pp. 481–487, May 2006.

    Article  Google Scholar 

  12. H. Rıza Ozçalık, “An efficient DC servo motor control based on neural noncausal inverse modeling of the plant,” Lecture Notes in Computer Science, Springer-Verlag, vol. 3972, pp. 1075–1083, 2006.

    Article  Google Scholar 

  13. A. Rubaai and R. Kotaru, “Online identification and control of a DC motor using learning adaptation of neural networks,” IEEE Trans. on Industry Applications, vol. 36, no. 3, pp. 935–942, May/June 2000.

    Article  Google Scholar 

  14. M. Fallahi and S. Azadi, “Adaptive control of a DC motor using neural network sliding mode control,” Proc. of the International Multi Conference of Engineers and Computer Scientists, vol. 2, IMECS-2009, March, 2009.

  15. J. Ye, “Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot,” Neuro Computing, vol. 71, no. 7–9, pp. 1561–1565, March 2008.

    Google Scholar 

  16. T. D. C. Thanh and D. C. Thien, “Hybrid neuro — PID controller of pneumatic artificial muscle manipulator for knee rehabilitation,” Proc. of International Symposium on Electrical & Electronics Engineering, pp. 20–29, October 2007.

    Google Scholar 

  17. A. Cozma and D. Pitica, “Artificial neural network and PID based control system for DC motor drives,” Proc. of IEEE 11th International Conference on Optimization of Electrical and Electronic Equipment, pp. 161–166, May 2008.

    Google Scholar 

  18. M. J. Burridge and Z. Qu, “An improved nonlinear control design for series DC motors,” Computers & Electrical Engineering, vol. 29, no. 2, pp. 273–288, March 2003.

    Article  MATH  Google Scholar 

  19. W. J. Jemai, H. Jerbi, and M. N. Abdelkrim, “Nonlinear state feedback design for continuous polynomial systems,” International Journal of Control, Automation, and Systems, vol. 9, no. 3, pp. 566–573, 2011.

    Article  Google Scholar 

  20. N. Matsui, T. Sugimoto, and H. Maenaka, “Stability of chopper-controlled DC series motor under regenerative braking,” Electrical Engineering in Japan, vol. 98, no. 5, pp. 40–47, 1978.

    Article  Google Scholar 

  21. A. Hussein, K. Hirasawa, and J. Hu, “Stability analysis of a DC motor system using universal learning networks,” Proc. of IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1285–1290, 2004.

    Google Scholar 

  22. G.-J. Wang, C.-T. Fong, and K. J. Chang, “Neuralnetwork-based self-tuning PI controller for precise motion control of PMAC motors,” IEEE Trans. on Industrial Electronics, vol. 48, no. 2, pp. 408–415, 2001.

    Article  Google Scholar 

  23. C. Gencer, A. Saygin, and I. Coskun, “DSP based fuzzy-neural speed tracking control of brushless DC motor,” Lecture Notes in Computer Science, Springer-Verlag, vol. 3949, pp. 107–116, 2006.

    Article  Google Scholar 

  24. F. M. EL-Khouly, A. M. Sharaf, A. S. Abdel-Ghaffar, and A. A. Mohammed, “An adaptive neural network speed controller for permanent magnet DC motor drives,” Proc. of the 26th Southeastern Symposium on System Theory, pp. 416–420, 1994.

    Chapter  Google Scholar 

  25. B. S. Ali, H. M. Hasanien, and Y. Galal, “Speed control of switched reluctance motor using artificial neural network controller,” Computational Intelligence and Information Technology, Springer-Verlag, vol. 250, Part 1, pp. 6–14, 2011.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering department, Centre for Advanced Research, Muthayammal Engineering College, Rasipuram, Tamilnadu, 637 408, India

    Masilamani Muruganandam

  2. Electronics and Communication Engineering department, Centre for Advanced Research, Mahendra Engineering College, Mallasamudram, 637503, Tamilnadu, India

    Muthusamy Madheswaran

Authors
  1. Masilamani Muruganandam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muthusamy Madheswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muthusamy Madheswaran.

Additional information

Recommended by Editorial Board member Shengyuan Xu under the direction of Editor Young-Hoon Joo.

Masilamani Muruganandam received his B.E degree in Electrical and Electronics Engineering from the Periyar University Salem, India, in 2003, and his M.E degree Power Electronics and Drives from the Anna University of Chennai, India, in 2005. He is currently working towards his doctoral degree at the Anna University Chennai, India. He has been a member of the faculty at Centre for Advanced Research, Muthayammal Engineering College, Rasipuram, Tamilnadu, India since 2005. His research interests include fuzzy logic and neural network applications to power electronics and drives and machine modeling. He is a life member of ISTE.

Muthusamy Madheswaran received his BE Degree from Madurai Kamaraj University in 1990, an ME Degree from Birla Institute of Technology, Mesra, Ranchi, India in 1992, both in Electronics and Communication Engineering. He obtained his Ph.D. degree in Electronics Engineering from the Institute of Technology, Banaras Hindu University, Varanasi, India, in 1999. At present he is a Principal of Mahendra Engineering College, Mallasamudram West, Tiruchengode, Namakkal Dist. Tamilnadu, India. He has authored over hundred and forty five research publications in international and national journals and conferences. His areas of interest are theoretical modeling and simulation of high-speed semiconductor devices for integrated optoelectronics application, Biooptics and Bio-signal Processing. He was awarded the Young Scientist Fellowship (YSF) by the State Council for Science and Technology, Tamilnadu, in 1994 and Senior Research Fellowship (SRF) by the Council of Scientific and Industrial Research (CSIR), Government of India in 1996. Also he has received YSF from SERC, Department of Science and Technology, Govt. of India. He is named in Marquis Who’s Who in Science and engineering in the year 2006. He is a life member of IETE, ISTE and IE (India) and also a senior member of IEEE.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Muruganandam, M., Madheswaran, M. Stability analysis and implementation of chopper fed DC series motor with hybrid PID-ANN controller. Int. J. Control Autom. Syst. 11, 966–975 (2013). https://doi.org/10.1007/s12555-012-9209-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-012-9209-y

Keywords

Search

Navigation