Proportional—Integral—Derivative Controller Design by Using Artificial Bee Colony,Harmony Search,and the Bees Algorithms

Abstract

This paper is an extended version of the paper presented in IPROMS 2009, which analysed the effect of the problem of dimensionality on the performance of a basic artificial bee colony, harmony search, and the bees algorithms on unimodal and multimodal well-known benchmark problems. A comparison of these algorithms was made in terms of the number of control parameters to be tuned. The algorithms considered were applied to proportional—integral—derivative (PID) controller tuning which involves the PID gains to be determined. Responses of the test systems to step input in terms of some metrics such as overshoot percentage, rising time, settling time and error are examined and also the effects of the disturbance on the control system performance and the system stability are analysed.

Keywords

artificial bee colony harmony search the bees algorithm PID controller design

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References

Holland

J. H.

Adaptation in Natural and Artificial Systems , 1975 (University of Michigan Press, Ann Arbor).

Kennedy

Eberhart

Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural networks, Piscataway, NJ, 1995, pp.1942-1948.

De Castro

L. N.

Timmis

Artificial Immune Systems: A New Computational Intelligence Approach , 2002, pp. 57–58. (Springer, New York).

Dorigo

Maniezzo

Colorni

Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Systems, Man, and Cybernetics - Part B 1996, 26(1) 29–41.

Geem

Z. W.

Kim

J. H.

Loganathan

G. V.

A new heuristic optimization algorithm: Harmony search. Simulation 2002, 76(2), 60–68.

Karaboga

An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

Pham

D. T.

Ghanbarzadeh

Koc

Otri

Rahim

Zaidi

The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.

Karaboga

Akay

Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In Proceedings of IPROMS 2009 Conference, Cardiff, UK, available from url: http://conference.iproms.org/artificial_bee_colony_abc_harmony_search_and_bees_algorithms_on_numerical_optimization.

Ziegler

J. B.

Nichols

N. B.

Optimum settings for automatic controllers. ASME Trans 64, 759–768.

10.

Sun

Parameter optimization of PID controller based on quantum-behaved particle swarm optimization algorithm. Complex Systems Applic., Modelling Control, and Simulations 2007, 14, 603–607.

11.

Puangdownreong

Sujitjorn

Obtanining an optimum PID controller via adaptive tabu search. ICANNGA 2007, LNCS, 2007 vol. 4432, pp. 745–755.

12.

Jinhua

Jian

Haifeng

Wang , Sun'an Self-organizing genetic algorithm based tuning of PID controllers. Information Sci., 2009, 179, 1007–1018.

13.

Karaboga

Basturk

A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. J. Global Optimization 2007, 39(3) 459–471.

14.

Karaboga

Basturk

, ‘On the performance of artificial bee colony (ABC) algorithm.’ Appl Soft Computing 8 (1) (2008): 687–697.

15.

Pham

Ghanbarzadeh

Koc

Otri

Rahim

Zaidi

, The bees algorithm ??;ndash; a novel tool for complex optimisation problems. In Proceedings of IPROMS 2006 Conference, Cardiff, UK.

16.

Pham

Sholedolu

, The bees algorithm with attraction to global best solutions. In Proceedings of IPROMS 2009 Conference, Cardiff, UK, available from url:.

17.

Omran

M. G. H.

Mahdavi

, ‘Global-best harmony search.’ Appl. Math. Computation 198 (2) (2008): 643–656.

18.

Astr??;ouml;m

Hagglund

, Benchmark systems for PID control. IFAC Digital Control: Past, Present and Future of PID Control, Terrassa, Spain. 2000 165–166