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Swarm Optimization based Controller for Temperature Control of a Heat Exchanger

by S.Rajasekaran, Dr.T.Kannadasan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 4
Year of Publication: 2012
Authors: S.Rajasekaran, Dr.T.Kannadasan
10.5120/4674-6790

S.Rajasekaran, Dr.T.Kannadasan . Swarm Optimization based Controller for Temperature Control of a Heat Exchanger. International Journal of Computer Applications. 38, 4 ( January 2012), 6-11. DOI=10.5120/4674-6790

@article{ 10.5120/4674-6790,
author = { S.Rajasekaran, Dr.T.Kannadasan },
title = { Swarm Optimization based Controller for Temperature Control of a Heat Exchanger },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 4 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number4/4674-6790/ },
doi = { 10.5120/4674-6790 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:39.921778+05:30
%A S.Rajasekaran
%A Dr.T.Kannadasan
%T Swarm Optimization based Controller for Temperature Control of a Heat Exchanger
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 4
%P 6-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In This paper uses an Attractive-Repulsive Particle Swarm Optimization (ARPSO) method for determining the optimal parameters of proportional-integral-derivative (PID) controller for temperature control of a shell and tube heat exchanger. Most of the heat exchange process is characteristic of nonlinear, large time delay and time varying. For such process it is very difficult to tune its controller parameters based on traditional PID tuning. The proposed method has excellent features, including high computational efficiency, quick stable convergence and easy implementation than standard PSO. In the proposed system, the ARPSO is implemented by MATLAB and compared with standard PSO and Genetic Algorithm (GA). The result shows that the proposed system has a more efficient in improving the step response characteristics such as reducing the steady state error, rise time; settling time and maximum peak overshoot in temperature control of a shell and tube heat exchanger.

References
  1. Mitsukura.Y, Yamamoto.T, and Kaneda.M. June 1999. A design of self-tuning PID controllers using a genetic algorithm, in Proc. Amer. Contr. Conf., San Diego, CA, pp. 1361–1365.
  2. Kennedy.J and Eberhart.R. 1995. Particle swarm optimization, Proc.IEEE Int. Conf. Neural Networks, vol. IV, Perth, Australia, pp.1942–1948.
  3. Eberhart.R.C and Shi.Y. May 1998. Comparison between genetic algorithms and particle swarm optimization, Proc. IEEE Int. Conf. Evol. Comput., Anchorage, AK, pp. 11–616.
  4. Hancock.P. 1994. An Empirical Comparison of Selection Methods in Evolutionary Algorithms, Evolutionary Computing, pp.80-95.
  5. Goldberg.D and Deb.K. 1991. A Comparative Analysis of Selection Schemes used in Genetic Algorithms, Foundations of Genetic Algorithms, pp.69-93.
  6. Huang. P.Y and Chen.Y.Y. 1997. Design of PID Controller for Precision Positioning Table Using Genetic Algorithms, Proceedings of the 36th IEEE Conference on Decision and Control, pp.2513-2514.
  7. Mitsukura.Y, Yamamoto.T, and Kaneda.M. 1999. A Design of Self-Tuning PID Controllers Using a Genetic Algorithm, Proceedings of the American Control Conference, pp.1361- 1365.
  8. Clerc M, Kennedy. J. 2002. The Particle Swarm: Explosion, Stability, and Convergence in Multi-Dimension Complex Space, IEEE Transactions on Evolutionary Computation, 16(1): 58-73
  9. Sun.J, Feng.B, Xu.W.B. 2004. Particle swarm optimization with particles having quantum behavior, Proc. of 2004 Congress on Evolution Computation, Piscataway, pp.325-331.
  10. Liang.Y.C, Kulturel-Konak.S, and Smith.A.E. May 2002. Meta- heuristics for the Orienteering Problem, Proceedings of the Congress on Evolutionary Computation, Honolulu, Hawaii, pp. 384-389.
  11. Venter.G, and Sobieszczanski-Sobieski.J. 2002. Particle Swarm Optimization, American Institute of Aeronautics and Astronautics, pp. 1202-1235.
  12. Riget.J and Vesterstrom.J.S. 2002. A Diversity Guided Particle Swarm Optimizer—the ARPSO, EVA-Life Technical Report.
  13. Hangos.K.M, Bokor.J, and Szederkényi.G,.2004. Analysis and control of nonlinear process control systems, Advanced Textbooks in Control and Signal Processing, 1st Edition, Ch. 4, Springer-Verlag London limited, pp. 55-61.
  14. Luyben.W.L. 1990. Process Modeling, Simulation and control for Chemical Engineers, 2nd Edition, Mc Graw-Hill Publishers.
  15. Macgregor.J.F, Wright.J, and M Hong.H. 1975.Optimal tuning of digital PID controller using dynamic stochastic models, Industrial Engineering and Chemical Process Design and Development, Vol. 14, 4, , pp. 398-402.
  16. Wang.P and Kwok.D.P.1994. Optimal Design of PID Process Controllers based on Genetic Algorithms, Control Engineers Practice, Vol. 2, 4, pp. 641-648.
  17. Lin.C.L, and Jan.H.Y, and Shieh.N.C. 2003. GA-based multiobjective PID control for a linear brushless DC motor, IEEE/ASME Trans. Mechatronics , vol.8, No. 1, pp. 56-65.
Index Terms

Computer Science
Information Sciences

Keywords

Heat exchangers PSO PID controller tuning