Application of Chaos Embedded PSO for PID Parameter Tuning

  • O. Tolga Altinoz Hacettepe University Bala Vocational School Electronics Technology Department, Ankara, Turkey
  • A. Egemen Yilmaz Ankara University Electronics Engineering Department, Ankara, Turkey
  • G. Wilhelm Weber Middle East Technical University Institute of Applied Mathematics, Ankara, Turkey

Abstract

Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.

References

[1] L.S. Coelho, A novel quantum particle swarm optimizer with chaotic mutation operator, Chaos, Solitons and Fractals, Vol.37, pp. 1409-1418, 2008
http://dx.doi.org/10.1016/j.chaos.2006.10.028

[2] L.S. Coelho, V.C. Mariani, A novel chaotic particle swarm optimization approach using Henon map and implicit filtering local search for economic load dispatch, Chaos, Solitons and Fractals, Vol.39, pp. 510-518, 2009
http://dx.doi.org/10.1016/j.chaos.2007.01.093

[3] S. Cong and Y. Liang, PID-Like neural network nonlinear adaptive control for uncertain multivariable motion control systems, IEEE Transactions on Industrial Electronics, 56(10):3872- 3879, 2009
http://dx.doi.org/10.1109/TIE.2009.2018433

[4] X.Y. Gao, L.Q. Sun, D.S. Sun, An enhanced particle swarm optimization algorithm, Information Technology Journal, Vol.8, pp. 1263-1268, 2009
http://dx.doi.org/10.3923/itj.2009.1263.1268

[5] H.N. Iordanou, B.W. Surgenor, Experimental evaluation of the robustness of discrete sliding mode control versus linear quadratic control, IEEE Transactions on Control Systems Technology, 5(2):254-260, 1997
http://dx.doi.org/10.1109/87.556029

[6] C. Jiejin, M. Xiaoqian, L. Lixiang, P. Haipeng, Chaotic particle swarm optimization for economic dispatch considering the generator constraints, Energy Conversion and Management, Vol.48, pp. 645-653, 2007
http://dx.doi.org/10.1016/j.enconman.2006.05.020

[7] J. Kennedy, R.C. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Networks, pp. 1942-1948, 1995

[8] B. Liu, L. Wang, Y.H. Jin, F. Tang, C.X. Huang, Improved particle swarm optimization combined with chaos, Chaos, Solitons and Fractals, 25, pp. 1261-1271, 2005
http://dx.doi.org/10.1016/j.chaos.2004.11.095

[9] G.A. Medrano-Cersa, Robust computer control of an inverted pendulum, IEEE Control Systems Magazine, 19(3):58-67, 1999
http://dx.doi.org/10.1109/37.768541

[10] Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, IEEE International Conference on Evolutionary Computation, pp. 69-73, 1998

[11] Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, Congress of Evolutionary Computing, pp. 1945-1950, 1999

[12] Y. Song, Z. Chen, Z. Yuan, New chaotic PSO-based neural network predictive control for nonlinear process, IEEE Transactions on Neural Networks, 18(2):595-601, 2007
http://dx.doi.org/10.1109/TNN.2006.890809

[13] J.M.T. Thompson, H.B. Stewart, Nonlinear Dynamics and Chaos, John Wiley and Sons, 2nd Edition, 2002

[14] T. Xiang, X. Liao, K.W. Wang, An improved particle swarm optimization combined with piecewise linear chaotic map, Applied Mathematics and Computation, pp. 1637-1645, 2007
http://dx.doi.org/10.1016/j.amc.2007.02.103

[15] G.W. van der Linder, P.F. Lambrechts, H-inf control of an experimental inverted pendulum with dry friction, IEEE Control Systems Magazine, 13(4):44-50, 1993
http://dx.doi.org/10.1109/37.229559
Published
2014-09-20
How to Cite
ALTINOZ, O. Tolga; YILMAZ, A. Egemen; WEBER, G. Wilhelm. Application of Chaos Embedded PSO for PID Parameter Tuning. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 2, p. 204-217, sep. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1403>. Date accessed: 24 nov. 2020. doi: https://doi.org/10.15837/ijccc.2012.2.1403.

Keywords

Particle Swarm Optimization (PSO); chaos theory; PID control; multidimensional optimization