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Application of Current Search to Optimum PIDA Controller Design

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DOI: 10.4236/ica.2012.34035    2,930 Downloads   4,362 Views   Citations


An application of the current search (CS), one of the most efficient metaheuristic optimization search techniques, to design the PIDA (proportional-integral-derivative-accelerated) controllers is proposed in this paper. The CS is applied to search for the optimum PIDA controller’s parameters. The obtained controllers are tested against nine benchmark systems collected by ?sstr?m and H?gglund considered as the hard-to-be-controlled plants and an automatic voltage regulator (AVR) system. As results, the optimum PIDA controllers can be successfully obtained by the CS and the responses of controlled systems are very satisfactory.

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The authors declare no conflicts of interest.

Cite this paper

D. Puangdownreong, "Application of Current Search to Optimum PIDA Controller Design," Intelligent Control and Automation, Vol. 3 No. 4, 2012, pp. 303-312. doi: 10.4236/ica.2012.34035.


[1] N. Minorsky, “Directional Stability of Automatically Steered Bodies,” American Society of Naval Engineering, Vol. 34, No. 2, 1922, p. 284.
[2] A. Dwyer, “Handbook of PI and PID Controller Tuning Rules,” Imperial College Press, London, 2003. doi:10.1142/p277
[3] S. Jung and R.C. Dorf, “Analytic PIDA Controller Design Technique for a Third Order System,” Proceedings of the 35th IEEE Conference on Decision and Control, Kobe, 11-13 December 1996, pp. 2513-2518.
[4] C. U. Thaiwasin, S. Sujitjorn, Y. Prempraneerach and J. Ngamwiwit, “Torsional Resonance Suppression via PIDA Controller,” Proceedings of the IEEE Region 10 Conference TENCON, Kuala Lumpur, Vol. 3, 2000, pp. 498-503.
[5] D.-Y. Ha, I.-Y. Lee, Y.-S. Cho, Y.-D. Lim and B.-K. Choi, “The Design of PIDA Controller with Pre-Compensator,” Proceedings of the IEEE International Symposium on ISIE, Pusan, Vol. 2, 2001, pp. 798-804.
[6] S. Sornmuang and S. Sujitjorn, “GA-Based PIDA Control Design Optimization with an Application to AC Motor Speed Control,” International Journal of Mathematics and Computers in Simulation, Vol. 4, No. 3, 2010, pp. 67-80.
[7] D. T. Pham and D. Karaboga, “Intelligent Optimisation Techniques,” Springer, London, 2000. doi:10.1007/978-1-4471-0721-7
[8] L. J. Fogel, A. J. Owens and M. J. Walsh, “Artificial Intelligence through Simulated Evolution,” John Wiley, Hoboken, 1966.
[9] F. Glover and M. Laguna, “Tabu Search,” Kluwer Academic Publishers, Dordrecht, 1997. doi:10.1007/978-1-4615-6089-0
[10] S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, Vol. 220, No. 4598, 1983, pp. 671-680. doi:10.1126/science.220.4598.671
[11] D. E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning,” Addison Wesley Publishers, Edmonton, 1989.
[12] M. Dorigo, “Optimization, Learning and Natural Algorithms,” PhD Thesis, Politecnico di Milano, 1992.
[13] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968
[14] Z. W. Geem, J. H. Kim and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, Vol. 76, No. 2, 2001, pp. 60-68. doi:10.1177/003754970107600201
[15] K. M. Passino, “Biomimicry of Bacterial Foraging for Distributed Optimization and Control,” IEEE Control System Magazine, Vol. 22, No. 3, 2002, pp. 52-67. doi:10.1109/MCS.2002.1004010
[16] M. M. Eusuff and K. E. Lansey, “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm,” Journal of Water Resource Planning and Management, Vol. 129, No. 3, 2003, pp. 210225. doi:10.1061/(ASCE)0733-9496(2003)129:3(210)
[17] D. T. Pham, A. Ghanbarzadeh, E. Ko?, S. Otri, S. Rahim and M. Zaidi, “The Bees Algorithm—A Novel Tool for Complex Optimisation Problems,” Proceedings of the IPROMS Conference, 2006, pp. 454-461.
[18] J. Qin, “A New Optimization Algorithm and Its Application—Key Cutting Algorithm,” Grey Systems and Intelligent Services, Nanjing, 10-12 November 2009, pp. 1537-1541.
[19] X. S. Yang, “Firefly Algorithms for Multimodal Optimization, Stochastic Algorithms,” Foundations and Applications SAGA 2009, Lecture Notes in Computer Sciences, Vol. 5792, 2009, pp. 169-178. doi:10.1007/978-3-642-04944-6_14
[20] R. Oftadeh, M. J. Mahjoob and M. Shariatpanahi, “A Novel Mata-Heuristic Optimization Algorithm Inspired by Group Hunting of Animals: Hunting Search,” Computers and Mathematics with Applications, Vol. 60, No. 1, 2010, pp. 2087-2098. doi:10.1016/j.camwa.2010.07.049
[21] X. S. Yang and S. Deb, “Engineering Optimization by Cuckoo Search,” International Journal of Mathematical Modeling and Numerical Optimization, Vol. 1, No. 4, 2010, pp. 330-343. doi:10.1504/IJMMNO.2010.035430
[22] A. Sukulin and D. Puangdownreong, “A Novel MetaHeuristic Optimization Algorithm: Current Search,” Proceedings of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED’ 12), Cambridge, 22-24 February 2012, pp. 125-130.
[23] D. Puangdownreong and A. Sukulin, “Obtaining an Optimum PID Controllers for Unstable Systems Using Current Search,” International Journal of Systems Engineering, Applications & Development, Vol. 6, No. 2, 2012, pp.188-195.
[24] K. J. ?str?m and T. H?gglund, “Benchmark Systems for PID Control,” IFAC Digital Control: Past, Present and Future of PID Control, Terrassa, 5-7 April 2000, pp. 165-166.
[25] Z. L. Gaing, “A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System,” IEEE Transactions on Energy Conversion, Vol. 19, No. 2, 2004, pp. 384-391. doi:10.1109/TEC.2003.821821

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