• Haytem Ali Department of Control Engineering, Collage of Electronic Technology, Baniwalid, Libya
  • Abdulgani Albagul Engineering and Information Technology Research Center, Baniwalid, Libya
  • Alhade Algitta Department of Control Engineering, Collage of Electronic Technology, Baniwalid, Libya




Particle Swarm Optimization, Ball and Beam System, PID Controller, PID Tuning Method


This paper introduces the application of an optimization technique, known as Particle Swarm Optimization (PSO) algorithm to the problem of tuning the Proportional-Integral-Derivative (PID) controller for a linearized ball and beam control system. After describing the basic principles of the Particle Swarm Optimization, the proposed method concentrates on finding the optimal solution of PID controller in the cascade control loop of the Ball and Beam Control System. Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. The efficiency of Particle Swarm Optimization algorithm for tuning the controller will be compared with a classical method, Trial and Error method. The comparison is based on the time response performance. The two tuning methods have been developed by simulation study using Matlab\ m-file software. The evaluations show that Evolutionary method Particle Swarm Optimization (PSO) algorithm gives a much better response than trial and error method.


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Peter E. Wellstead, “Introduction to Physical System Modelling”, Academic Press Limited, pp. 221-227, 2000.

M. A. Rana, Z. Usman, Z. Shareef, “Automatic Control of Ball and Beam System Using Particle Swarm Optimization,” CINTI 2011 • 12th IEEE International Symposium on Computational Intelligence and Informatics, pp. 529-534, 2011. DOI: https://doi.org/10.1109/CINTI.2011.6108563

Wei Wang, “Control of a Ball and Beam System,” M. Sc. Thesis, University of Adelaide, AUSTRALIA, June 2007.

M. F. Rahmat, H. Wahid, and N. A. Wahab, “Application of intelligent controller in a ball and beam control system,” International journal on smart sensing and intelligent systems, vol. 3, pp. 45-60, 2010. DOI: https://doi.org/10.21307/ijssis-2017-378

Ziegler J. G. et al, “Optimum settings for automatic controllers”, Transactions of ACME, 1942, vol.64, pp. 759-768.

Astrom K. J. and Hagglund T., “Automatic tuning of simple regulators with specifications on phase and amplitude margins”, Automatica, 1984, vol. 20, pp. 645-651. DOI: https://doi.org/10.1016/0005-1098(84)90014-1

D’Azzo J. J. and Houpis C.H., “Linear control system analysis and design: conventional and modern”, McGraw-Hill Series in Electrical and Computer Engineering, New York 1995, 4th edition.

Astrom K. J. and Hagglund T., “PID Controllers: Theory, Design, and Tuning”, Instrument Society of America, 1995, 2nd ed., pp. 134-229.

Zang H., Zhang S. and Hapeshi K., “A review of nature-inspired algorithms”, Journal of Bionic Engineering, September 2010, vol. 7, Supplement 1, pp. S232-S237. DOI: https://doi.org/10.1016/S1672-6529(09)60240-7

Q. Bai, “Analysis of Particle Swarm Optimization Algorithm”, Computer and Information Science, vol 3 (1), February 2010, pp. 180-184. DOI: https://doi.org/10.5539/cis.v3n1p180

M. Rahmani, “Particle swarm optimization of artificial neural networks for autonomous robots,” M. Sc. Thesis, Chalmers University of Technology, SWEDEN, 2008.

J. Im, J. Park, “Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics,” Chinese Journal of Aeronautics, pp. 112-121, 2013. DOI: https://doi.org/10.1016/j.cja.2012.12.022

W. H. Lim and N. A. Mat, “Particle swarm optimization with increasing topology connectivity,” Engineering Applications of Artificial Intelligence, pp. 80–102, 2014. DOI: https://doi.org/10.1016/j.engappai.2013.09.011

D. Tian and N. Li, “Fuzzy Particle Swarm Optimization Algorithm,” International Joint Conference on Artificial Intelligence, pp. 263-267, 2009. DOI: https://doi.org/10.1109/JCAI.2009.50

M. Amjad, Kashif M.I., S. S Abdullah and Z.Shareef, “A Simplified Intelligent Controller for Ball and Beam System,” International Conference on Education Technology and Computer (ICETC), vol. 3, pp. 494-498, 2010. DOI: https://doi.org/10.1109/ICETC.2010.5529491




How to Cite

Ali, H., Albagul, A., & Algitta, A. (2018). OPTIMIZATION OF PID PARAMETERS BASED ON PARTICLE SWARM OPTIMIZATION FOR BALL AND BEAM SYSTEM . International Journal of Engineering Technologies and Management Research, 5(9), 59–69. https://doi.org/10.29121/ijetmr.v5.i9.2018.289