PID Controller Tuning Using Soft Computing Techniques

  • Nikhileshwar Prasad Adhikari
  • Amit Gupta
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)

Abstract

The aim of this paper is to design a position control of a DC motor by selection of a PID controller using genetic algorithm. This paper compares two kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler and Nichols’ method. This proposed method could be applied to the higher order system also.

Keywords

DC shunt motor PID controller Ziegler Nichols method Genetic algorithm 

References

  1. 1.
    Ogata K (1987) Discrete-time control systems. University of Minnesota, Prentice HallGoogle Scholar
  2. 2.
    Soltoggio A (2005) An enhanced GA to improve the search process reliability in tuning of control systems. In: Proceedings of the 2005 conference genetic and evolutionary computation, GECCO’05, Washington, pp 2165–2172Google Scholar
  3. 3.
    Chen QG, Wang N (2005) The distribution population-based genetic algorithm for parameter optimization PID controller. Acta Automatica Sinica 31:646–650Google Scholar
  4. 4.
    Astrom K, Hagglund T (1995) PID controllers: theory, design and tuning. Instrument Society of America. Research triangle park, NCGoogle Scholar
  5. 5.
    Chowdhuri S, Mukherjee A (2000) An evolutionary approach to optimize speed controller of dc machines. In: Proceedings of IEEE international conference on industrial technology, Cilt 2, 682–687Google Scholar
  6. 6.
    Chambers L (1999) Practical handbook of genetic algorithms: complex coding systems. CRC, Boca RatonGoogle Scholar
  7. 7.
    Pelczewski PM, Kunz UH (1990) The optimal control of a constrained drive system with brushless dc motor. Ind Electron IEEE Trans on 37(5): 342–348Google Scholar
  8. 8.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Pub. Co, BostonGoogle Scholar
  9. 9.
    Chipperfield AJ, Fleming PJ, Pohlheim H, Fonseca CM (1994) A genetic algorithm toolbox for MATLAB. In: Proceedings international conference on systems engineering, coventry, UK, 6–8 Sept 1994Google Scholar
  10. 10.
    Kristinsson K, Dumont GA (1992) System identification and control using genetic algorithms. Syst Man Cybern IEEE Trans on 22(5): 1033–1046Google Scholar
  11. 11.
    Mahony TO, Downing CJ, Fatla K (2000) Genetic algorithm for PID parameter optimization: minimizing error criteria. Proc Control Instrum 2000: 26–28Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Nikhileshwar Prasad Adhikari
    • 1
  • Amit Gupta
    • 2
  1. 1.Department of Electrical Engineering, Control SystemGyan Ganga College of TechnologyJabalpurIndia
  2. 2.Department of Electrical EngineeringTakshshila Institute of Engineering and TechnologyJabalpurIndia

Personalised recommendations