Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology


Performance Enhancement of PID Controllers by Modern Optimization Techniques for Speed Control of PMBL DC Motor

1M. Antony Freeda Rani and 2B. Sankaragomathi
1Department of Electrical and Electronics Engineering, Udaya School of Engineering, Nagercoil, Tamil Nadu, India
2Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, India
Research Journal of Applied Sciences, Engineering and Technology   2015  10:1154-1163
http://dx.doi.org/10.19026/rjaset.10.1883  |  © The Author(s) 2015
Received: February ‎16, ‎2015  |  Accepted: March ‎1, ‎2015  |  Published: August 05, 2015

Abstract

Permanent Magnet Brushless DC motor (PMBL DC) is used in a large number of industrial and automotive applications because of their high efficiency, compactness and excellent reliability. However to design an efficient PMBL DC motor, it is necessary to provide an effective controller that has to reduce the overshoot, settling and rise time. In this study, an improved PID controller has been designed by optimizing the parameters of PID controller based on two advanced optimization techniques ANFIS and Cuckoo Search optimization for speed control of a PMBL DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The PMBL DC motor is modeled in SIMULINK implementing the algorithms in MATLAB and the performance evaluation has been studied.

Keywords:

ANFIS , cuckoo search optimization, permanent magnet brushless DC motor, PID controller,


References

  1. Aggrawal, A., A.K. Mishra and A. Zeeshan, 2014. Speed control of DC motor using particle swarm optimization technique by PSO tunned PID and FOPID. Int. J. Eng. Trends Technol. (IJETT), 16(2): 72-79.
    CrossRef    
  2. Ali, A. and S. Majhi, 2006. Design of optimum PID controller by bacterial foraging strategy. Proceeding of IEEE International Conference on Industrial Technology, pp: 601-605.
    CrossRef    
  3. Amanullah, M.D., M. Jain, P. Tiwari, S. Gupta and G. Kumari, 2014. Optimization of PID parameter for position control of DC-motor using multi-objective genetic algorithm. Int. J. Innov. Res. Elect. Electron. Instrum. Control Eng., 2(6): 1644-1654.
  4. Bindu, R. and M.K. Namboothiripad, 2012. Tuning of PID controller for DC servo motor using genetic algorithm. Int. J. Emerg. Technol. Adv. Eng., 2(3): 310-314.
  5. Chan, Y.F., M. Moallem and W. Wang, 2007. Design and implementation of modular FPGA-based PID controllers. IEEE T. Ind. Electron., 54(4): 1898-1906.
    CrossRef    
  6. Emami, S.A., M.B. Poudeh and S. Eshtehardiha, 2008. Particle Swarm Optimization for improved performance of PID controller on Buck converter. Proceeding of IEEE International Conference on Mechatronics and Automation.
    CrossRef    
  7. Fanghua, Z. and Y. Yangguang, 2009. Novel forward-flyback hybrid bidirectional DC-DC converter. IEEE T. Ind. Electron., 56: 1578-1584.
    CrossRef    
  8. Febin Daya, J.L., V. Subbiah, A. Iqbal and P. Sanjeevikumar, 2013. Novel wavelet-fuzzy based indirect field oriented control of induction motor drives. J. Power Electron., 13: 656-668.
    CrossRef    
  9. Gadoue, S.M., D. Giaouris and J.W. Finch, 2007. Genetic algorithm optimized PI and fuzzy sliding mode speed control for DTC drives. Proceeding of the World Congress on Engineering, 1: 2-4.
  10. Ibtissem, C., L. Noureddine and B. Pierre, 2012. Tuning PID controller using multiobjective ant colony optimization. Appl. Comput. Intell. Soft Comput., 2: 1-7.
    CrossRef    
  11. José Carlos, G.R., V.S. Ernesto and G.G. Jaime, 2010. Optimization of PID parameter for position control of DC-motor using multi-objective genetic algorithm. Sensors, pp: 6901-6947.
  12. Kishnani, M., S. Pareek and R. Gupta, 2014. Comparison of different performance index factor for ABC-PID controller. Int. J. Electron. Electr. Eng., 7: 177-182.
  13. Mitra, R. and S. Singh, 2013. Optimal fuzzy supervised PID controller using ant colony optimization algorithm. Adv. Electron. Electr. Eng. Res., 3: 553-560.
  14. Navidi, N., M. Bavafa and S. Hesami, 2012. A new approach for designing of PID controller for a linear brushless DC motor with using ant colony search algorithm. Proceeding of Asia-Pacific Power and Energy Engineering Conference (APPEEC, 2009), pp: 1-5.
  15. Pooja, S. and G. Rajeev, 2014. Tuning of PID controller for A linear brushless DC motor using swarm intelligence technique. Int. J. Eng. Res. Appl., 4: 125-128.
  16. Rodriguez, F. and A. Emadi, 2007. A novel digital control technique for brushless DC motor drives. IEEE T. Ind. Electron., 54: 2365-2373.
    CrossRef    
  17. Singh, B. and S. Singh, 2009. State of the art on permanent magnet brushless DC motor drives. J. Power Electron., 9: 1-17.

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved