CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

Speed Control of a Real Time D.C. Shunt Motor Using SA Based Tuning of a PID Controller

by N.Anantharaman, Atal.A.Kumar, S.M.Girirajkumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 11
Year of Publication: 2010
Authors: N.Anantharaman, Atal.A.Kumar, S.M.Girirajkumar

N.Anantharaman, Atal.A.Kumar, S.M.Girirajkumar . Speed Control of a Real Time D.C. Shunt Motor Using SA Based Tuning of a PID Controller. International Journal of Computer Applications. 5, 11 ( August 2010), 20-26. DOI=10.5120/954-1331

@article{ 10.5120/954-1331,
author = { N.Anantharaman, Atal.A.Kumar, S.M.Girirajkumar },
title = { Speed Control of a Real Time D.C. Shunt Motor Using SA Based Tuning of a PID Controller },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 5 },
number = { 11 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { },
doi = { 10.5120/954-1331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T19:54:01.727417+05:30
%A N.Anantharaman
%A Atal.A.Kumar
%A S.M.Girirajkumar
%T Speed Control of a Real Time D.C. Shunt Motor Using SA Based Tuning of a PID Controller
%J International Journal of Computer Applications
%@ 0975-8887
%V 5
%N 11
%P 20-26
%D 2010
%I Foundation of Computer Science (FCS), NY, USA

The proposed work deals with optimal tuning of a Proportional-Integral-Derivative (PID) controller for speed control of a DC shunt motor. PID controllers are widely used in industrial plants because of their simplicity and robustness. Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. So the control engineers are on look for automatic tuning procedures. The performance of Ziegler-Nichols method, one of the widely accepted conventional methods has been compared and analyzed with the intelligent tuning technique called the Simulated Annealing method (SA). The results establishes that tuning the PID controller using SA technique which comes under evolutionary programming has proved its excellence in giving better results by improving the steady state characteristics and performance indices.

  1. Afzalian.A,D.A.Linkens,2000,Training of neuro fuzzy power system stabilisers using genetic algorithms,"International Journal of Electrical Power and Energy Systems,vol.22,no.2,pp.93-102.
  2. Angeline, P.J.,1998: Using Selection to Improve Particle Swarm Optimization, IEEE int. Conf. 4- 9, pp. 84-89.
  3. Asriel U. Levin and Kumpati S. Narendra,1996, Control of nonlinear dynamical systems using Neural Networks- Part II : observability, identification and control, IEEE Transactions on Neural Networks, Vol. 7, No. 1.
  4. Astrom,K.J.,Hagglund,T.,1995:PIDcontrollers:Theory, Design, and Tuning. Instruments Society of America. 2edn.
  5. Astrom K.J, T.Hagglund,2001, The future of PID control, ControlEng.Pract.9(11)pp.1163–1175.
  6. Chen.S, R.H. Istepanian and J.Wu,1999, “ Optimizing stability bounds of finite-precision PID controllers using adaptive simulated annealing”, Proceedings of the American control conference ,San Diego, California .
  7. Chen.S and B.L.Luk,1999, "Adaptive simulated annealing for optimization in signal processing applications", Journal of Engineering and electronics, Vol. 79,pp. 117-128.
  8. DorigoM,ManiezzoV,ColorniA.,1996The ant system:optimization by a colony of cooperating agents.IEEETransSystManCybernetB;26(1):29–41.
  9. Dubey.M,P.Gupta,2005 ,Design of Genetic Algorithm Based Robust Power System Stabilizer,"International Journal of Computational Intelligence,vol.2,no.1,pp.48-52.
  10. Ender,D.B.,1993,Processcontrolperformance:.Control.Eng. pp180–190.
  11. GarciaC.E,D.M.Prett,M.Morari,1989,Model predictive control;Theory and practice-A survey," Automatica,vol.25,no.3,pp.333-348.
  12. Kennedy,J. and Eberhart,R.,1995: Particle Swarm Optimization in Proc. IEEE int. Conf. Neural Networks, vol. IV, Perth, Australia
  13. Matsummura,S.,1998: Adaptive Control for the SteamTemperature of Thermal Power Plants,Proceedings the 1993 IEEE on Control applications pp.1105-1109.
  14. Mehrdad Salami and Greg Cain,1995, An adaptive PID controller based on Genetic algorithm processor, Genetic algorithms in engineering systems: Innovations and applications, 1214, Conference publication No. 414, IEEE.
  15. Mwembeshia.M.M,C.A.Kenta,S.Salhi,2004,A genetic algorithm based approach to intelligent modelling and control of pH in reactors,"Computers and Chemical Engineering,vol.28,pp.1743-1757.
  16. O’Dwyer,A.,2000,PIandPIDControllerTuningRulesforTimeDelayProcesses:aSummary.Tech.Rep.AOD00-01 Ver.1,DublinInstituteofTechnology,Ireland.
  17. Qin S.J,T.A.Badgwell,1997,An over view of industrial model predictive control technology," AIChESymposium Series.
  18. Simon Fabri and Visakan Kadirkamanathan,1996 Dynamic structure neural networks for stable adaptive control of nonlinear systems, IEEE Transactions on Neural Networks, Vol. 7, No. 5, September.
  19. Su Whan Sung, In-Beum Lee and Jitae Lee,1995, Modified Proportional-Integral Derivative (PID) Controller and a New Tuning Method for the PID Controller, Ind. Eng. Chem. Res., 34, pp. 4127-4132.
  20. Varela L.R, R.A.Ribeiro and F.M.Pires,2002, “Simulated annealing and fuzzy optimization”, Proceedings of the 10th Mediterranean conference on control and automation- MED2002,Portugal, July 9-12.
  21. Vlachos .C,D.Williams,J.B.Gomm,2002,Solution to the Shell standard control problem using genetically tuned PID controllers," Control Engineering Practice,vol.10,no.2,pp.151-163,2002.
  22. Vieira.J,F.MorgadoDias,A.Mota,2004,Artificial Neural Networks and Neuro fuzzy Systems for Modeling and Controlling Real Systems: A Comparative Study,"Engineering Applications of Artifcial Intelligence,vol.17,pp.265-273.
  23. Wah.B and Y. Chen,2000, Constrained genetic algorithms and their applications in nonlinear constrained optimization, In Proceedings of International conference on tools with artificial intelligence, IEEE, pp. 286-293.
  24. Yu,C.C.,1999 :Auto-tuning of PID Controllers. Berlin: Springer 7edn .
  25. Ziegler J.G,N.B.Nichols,1942,Optimum setting for AutomaticControllers," Trans.ASME,vol.64,pp.759-768.
  26. Zwe-Lee Gaing,2002, “ A particle swarm optimization approach for optimum design of PID controller in AVR system”, IEEE transactions on energy conversion, November 6.
Index Terms

Computer Science
Information Sciences


PID Robust Conventional techniques SA evolutionary programming D.C shunt motor