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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
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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

    	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"


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.


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