Application of Chaos Embedded PSO for PID Parameter Tuning
AbstractProportional-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.
 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
 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
 X.Y. Gao, L.Q. Sun, D.S. Sun, An enhanced particle swarm optimization algorithm, Information Technology Journal, Vol.8, pp. 1263-1268, 2009
 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
 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
 J. Kennedy, R.C. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Networks, pp. 1942-1948, 1995
 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
 G.A. Medrano-Cersa, Robust computer control of an inverted pendulum, IEEE Control Systems Magazine, 19(3):58-67, 1999
 Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, IEEE International Conference on Evolutionary Computation, pp. 69-73, 1998
 Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, Congress of Evolutionary Computing, pp. 1945-1950, 1999
 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
 J.M.T. Thompson, H.B. Stewart, Nonlinear Dynamics and Chaos, John Wiley and Sons, 2nd Edition, 2002
 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
 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
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.