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PID Tuning using Elite Multi-Parent Crossover Genetic Algorithm

Reshmi P. Pillai, Sharad Jadhav, M. D. Patil Published in Algorithm

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/icwac1305
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  1. Reshmi P Pillai, Sharad Jadhav and M D Patil. Article: PID Tuning using Elite Multi-Parent Crossover Genetic Algorithm. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013 ICWAC(1):23-27, June 2013. BibTeX

    @article{key:article,
    	author = "Reshmi P. Pillai and Sharad Jadhav and M. D. Patil",
    	title = "Article: PID Tuning using Elite Multi-Parent Crossover Genetic Algorithm",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013",
    	year = 2013,
    	volume = "ICWAC",
    	number = 1,
    	pages = "23-27",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Proportional-Integral-Derivative (PID) controllers have been widely used in process industry for decades from small industry to high technology industry. But they still remainpoorly tuned by use of conventional tuning methods. Conventional technique like Zeigler-Niclos method does not give an optimized value for PID controller parameters. In this paper we optimize the PID controller parameter using Genetic Algorithm(GA), which isa stochastic global search method that replicates the process of evolution. Using genetic algorithms to perform the tuning of the controller will result in theoptimum controller being evaluated for the system every time. The GA is basicallybased on an iterative process of selection, recombination, mutation and evaluation. Multi-parent Crossover Algorithm with Discrete Recombination is implemented in this paper along with recommendation for further work. This algorithm uses different replacement strategy as compared to Elite Multi-Parent Crossover Evolutionary Optimization Algorithm (EMPCOA) therby increasing population diversity thus reducing the number of iterations required. Elitism is also known to increase speed and ensures the good solution once found is passed on to the next generation.

Reference

  1. K. S. Tang,Kim Fung Man, Guanrong Chen and Sam Kwong, "An Optimal Fuzzy PID Controller", IEEE Transactions On Industrial Electronics, vol 48, no 4, pp. 757-765, 2001
  2. Ho W. K. , Gan O. P. , Tay E. B. and Ang E. L. , "Performance and gain and phase margins of well-known PID tuning formulas", IEEE Transactions on Control Systems Technology, vol. 4, no. 4, pp. 473–477, 1996.
  3. Aidan O'Dwyer. 2006 PI and PID controller tuning rules: an overview and personal perspective. Proceedings of the {IET} Irish Signals and Systems Conference.
  4. K. S. Tang,Kim Fung Man, Guanrong Chen and Sam Kwong. 2001An Optimal Fuzzy PID Controller. IEEE Transactions On Industrial Electronics.
  5. Holland J. H. , "Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence", Ann Arbor, MI: University of Michigan Press 1975, Second edition, Cambridge, MA: The MIT Press, 1992.
  6. Teo Lian Seng, Marzuki Bin Khalid and Rubiyah Yusof. 1999. Tuning of a Neuro-Fuzzy. IEEE} Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics
  7. Kiam Heong Ang, Gregory Chong and Yun Li, "PID Control System Analysis, Design and Technology", IEEE Transactions On Control Systems Technology, vol 13, no 4, pp. 555-576, 2005.
  8. Dazhi Jiang, Yulin Du, Jiali Lin, Zhijian Wu. 2010Multi-parent Crossover Algorithm with Discrete Recombination. IEEE.
  9. R. Michalski, J. Carbonell and T. Mitchell, "Machine Learning: An Artificial Intelligence Approach", Los Altos, Morgan Kaufmann, 1986.
  10. Guo Tao and Kang Li-Shan, "A new Evolutionary Algorithm for Function Optimization", Wuhan University Journal of Natural Sciences, vol. 4, no. 4, pp. 409- 414, 1999.
  11. Xiaoyi Che, Youxin Luo and Zhaoguo Chen, "Optimization for PID control parameters on hydraulic servo control system based on the elite multi-parent crossover evolutionary algorithm" , International Conference on Measuring Technology and Mechatronics Automation, IEEE Computer Society, pp. 845- 848, 2010.

Keywords

PID tuning, Genetic Algorithm, Multi-parent crossover, Elite crossover, Discrete recombination