Optimal Tuning of PID Controller using Adaptive Hybrid Particle Swarm Optimization Algorithm
AbstractParticle swarm optimization (PSO) has proved its ability as an efficient search tool in many optimization problems. However, PSO is easy to be trapped into local minima due to its mechanism in information sharing. Under this circumstance, all the particles could quickly converge to a position by the attraction of the best particle; all particles could hardly be improved. To overcome premature convergence of the standard PSO algorithm, this paper presents an adaptive hybrid PSO, namely (AHPSO) by employing an adaptive mutation operator for local best particles instead of applying the mutation operator to the global best particle as has been done in previous work. The developed algorithm is a new approach which allows the swarm to be more diverse by making better exploration of the local search space instead of global search space investigated by previous researchers. The proposed algorithm holds on the properties of simple structure, fast convergence, and at the same time enhances the variety of the population, and extends the search space. It is applied to self-tuning of proportional-integral-derivative-(PID) controller in the ball and hoop system which represents a system of complex industrial processes. The results are compared with those obtained by applying standard PSO, and adaptive hybrid PSO based on global best particles. It has been shown that the developed AHPSO local best algorithm is faster in convergence and the obtained results are proved to have higher fitness than the other two algorithms.
 Conradie A., Miikkulainen R., and Aldrich C., "Adaptive Control Utilizing Neural Swarming", n Proceedings of the Genetic and Evolutionary Computation Conferences, USA, 2002.
 Hossein Shayeghi, Heidar Ali Shayanfar and Aref Jalili," Multi Stage Fuzzy PID Load requency Controller in a Restructured Power System", Journal of Electrical Engineering, ol. 58, No.. 2, pp. 61-70, 2007.
 Saban Cetin, and Ozgür Demir," Fuzzy PID Controller with Coupled Rules for a Nonlinear uarter Car Model", World Academy of Science, Engineering and Technology Vol. 41, p.238-241, 2008.
 Aye Aye Mon," Fuzzy Logic PID Control of Automatic Voltage Regulator System", Proceedings f PWASET, Vol. 38, Feb., 2009.
 Cipperfield A. Flemming P., and Fonscea C., "Genetic Algorithms for Control System Engineering", n Proceedings Adaptive Computing in Engineering Design Control, pp- 128-133, 994.
 Kennedy J. and Eberhart C., "Particle Swarm Optimization", Proceedings of the IEEE nternational Conference on Neural Networks, Australia, pp. 1942-1948, 1995.
 Oliveira, P. M., Cunha, J. B., and Coelho, J. o. P., "Design of PID controllers using the article Swarm Algorithm.", Twenty-First IASTED International Conference: Modeling, dentification, and Control (MIC 2002), Innsbruck, Austria. 2002.
 Yi-Tung Kao and Erwie Zahara," A hybrid genetic algorithm and particle swarm optimization or multimodal functions", Applied Soft Computing Vol. 8, pp 849-857, 2008.
 Robinson, J., Sinton, S., and Rahmat-Samii, Y., " Particle swarm, genetic algorithm, and heir hybrids: optimization of a profiled corrugated horn antenna", IEEE International ymposium on Antennas & Propagation. San Antonio, Texas. June, 2002.
 H. A. Kamal, " A new integrated GA/PSO Algorithm for Optimal tuning of PID Controller", he Mediterranean Journal of Measurement and Control, Vol. 6, No. 1, pp.18-24, January 010.
 M. Rashid and A. Rauf Baig, " A genetic programming based adaptable evolutionary hybrid article swarm optimization algorithm", International Journal of Innovative Computing, nformation and Control (ICIC), Vol. 6, Nu. 1, January 2010.
 Angeline, P. J., "Using selection to improve particle swarm optimization", Proceedings of he IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska, USA. 998.
 Lvbjerg, M., Rasmussen, T., and Krink, T, "Hybrid particle swarm optimizer with breeding nd subpopulations", Proceedings of the third Genetic and Evolutionary Computation onference (GECCO), Vol. 1, pp. 469-476, 2001.
 Miranda, V., and Fonseca, N.," New evolutionary particle swarm algorithm (EPSO) applied o voltage/VAR control", The 14th Power Systems Computation Conference (PSCC'02), eville, Spain, June, 2002.
 Lvbjerg, M., and Krink, T., "Extending particle swarms with self-organized criticality", roceedings of the Fourth Congress on Evolutionary Computation (CEC-2002).
 Blackwell, T., and Bentley, P. J., (2002). "Don't push me ! Collision-avoiding swarms". EEE Congress on Evolutionary Computation, Honolulu, Hawaii USA, 2002.
 Parsopoulos, K. E., and Vrahatis, M., "On the computation of all global minimizers through article swarm optimization", IEEE Transactions on Evolutionary Computation, (accepted or special issue on PSO, 2004.
 J. Chen, Z. Ren and X. Fan, "Particle swarm optimization with adaptive mutation and its pplication research in tuning of PID parameters," in Proc. 1st International Symposium n Systems and Control in Aerospace and Astronautics, pp. 990-994, 2006.
 H. Wang, Y. Liu C. H. Li, and S. Y. Zeng, "A Hybrid Particle swarm algorithm with Cauchy utation," IEEE Swarm Intelligence Symposium, Honolulu, Hawaii, pp. 356-360, 2007.
 Pant, M. Thangaraj, R. Abraham, A., "Particle swarm optimization using adaptive mutation," n Proc. 19th International Conference on Database and Expert Systems Application, p. 519-523, 2008.
 Jun Tang, and, X. Zhao, "A Hybrid Particle Swarm Optimization with Adaptive Local earch", journal of networks, Vol. 5, No.4, April 2010.
 Fatih Ta?getiren M and Yun-Chia Liang, "A Binary Particle Swarm Optimization Algorithm or Lot Sizing Problem", Journal of Economic and Social Research, Vol.5 No.2, pp. 1-20, 004.
 C. Li, S. Yang and I. A. Korejo. "An Adaptive Mutation operator for Particle Swarm". roceedings of the 2008 UK Workshop on Computational Intelligence, pp. 165-170, 2008.
 H. Wang, Y. Liu C. H. Li, and S. Y. Zeng, "A hybrid particle swarm algorithm with Cauchy utation," IEEE Swarm Intelligence Symposium, Honolulu, Hawaii, pp. 356-360, 2007.
 I. Griffin, "On-line PID Controller Tuning using Genetic Algorithms", MSc. Thesis School f Electronic Engineering Dublin City University, 2003.
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.