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Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms

by Nitish Katal, Sanjay Kr. Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 2
Year of Publication: 2012
Authors: Nitish Katal, Sanjay Kr. Singh
10.5120/8860-2822

Nitish Katal, Sanjay Kr. Singh . Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms. International Journal of Computer Applications. 56, 2 ( October 2012), 1-5. DOI=10.5120/8860-2822

@article{ 10.5120/8860-2822,
author = { Nitish Katal, Sanjay Kr. Singh },
title = { Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 2 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number2/8860-2822/ },
doi = { 10.5120/8860-2822 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:48.726018+05:30
%A Nitish Katal
%A Sanjay Kr. Singh
%T Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 2
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the performance comparison between the various soft computing techniques used for optimization of the PID controllers, implemented for speed control system for a DC motor. PID controllers are extensively used in industrial control because of their simplicity and robustness, but when industrial control is imperilled by external glitches, leads to the instability of the system. PID controller optimization using soft-computing algorithms lays emphases on obtaining the best possible PID parameters for improving the stability of the system. The PID controller has been implemented for speed control of a DC motor and the results obtained from optimization using soft-computing are compared with the ones derived from the Ziegler-Nichols method, and comparatively better results are obtained in Stimulated Annealing case.

References
  1. Wang, J. B. , Control of Electric Machinery. Gau Lih Book co. , Ltd, Taipei Taiwan, 2001.
  2. G. Haung and S. Lee, "PC based PID speed control in DC motor," IEEE Conf. SALIP-2008, pp. 400-408, 2008.
  3. Ziegler, J. G and Nichols, N. B. (1942). "Optimum settings for automatic controllers. " Transactions of the ASME. 64. pp. 759–768.
  4. Goodwin, G. C. , Graebe, S. F. and Salgado, M. E. 2001. Control System Design, Prentice Hall Inc. , New Jersey.
  5. Norman S. Nise, 2003 , Control System Engineering, 4th Edition,
  6. Deb, Kalyanmoy. "Multi-Objective Optimization Using Evolutionary Algorithms. " John Wiley & Sons, 2001.
  7. Kickpatrik S. , Gelatt C. D. , Vecchi M. P. (1983), "Optimization by Stimulated Annealing", Science (220) 4508, p-671-680,
  8. Granville, V. ; Krivanek, M. ; Rasson, J. -P. (1994). "Simulated annealing: A proof of convergence". IEEE Transactions on Pattern Analysis and Machine Intelligence 16
  9. Abdullah Konak, David W. Coit, Alice E. Smith ,"Multi-objective optimization using genetic algorithm", Reliability Engineering and Safety System, 91 (2006) 992-1007, Elsevier Ltd.
  10. Berrsimas D. , Tsitsiklis, " Stimulated Annealing" (1993), Statistical Science, Vol. 8, No. 1, 10-15
  11. MATLAB and SIMULINK Documentation
Index Terms

Computer Science
Information Sciences

Keywords

PID Controllers Controller Optimization DC Motor Genetic Algorithms Stimulated Annealing Multi-objective Genetic Algorithms

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