Comparative performance analysis of GA, PSO, CA and ABC algorithms for ractional PIλDμ controller tuning

This paper presents a comparison of some well-known global optimization techniques' performance. The purpose of the comparison is to find the best algorithm for optimization. Parameters optimization was conducted in order to achieve better results in PID fractional controller. The proposed techniques are Genetic algorithm (GA), particle swarm optimization (PSO), culture algorithm (CA) and artificial bee colony algorithm (ABC), these techniques are used to approximate the parameters of the fractional order PIλDμ controller.

[1]  M. Senthil Arumugam,et al.  Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution , 2010 .

[2]  Worthy N. Martin,et al.  Foundations of Genetic Algorithms 6 (Foga-6) , 2001 .

[3]  Haluk Gozde,et al.  Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system , 2011, J. Frankl. Inst..

[4]  M. Korkmaz,et al.  Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[5]  Lingbo Zhang,et al.  A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems , 2012, Neurocomputing.

[6]  Serdar Ethem Hamamci.  Stabilization using fractional-order PI and PID controllers , 2007 .

[7]  Sumit Mukherjee,et al.  Comparative studies on decentralized multiloop PID controller design using evolutionary algorithms , 2012, 2012 Students Conference on Engineering and Systems.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Xiaoyong Liu,et al.  Optimization design on fractional order PID controller based on adaptive particle swarm optimization algorithm , 2015, Nonlinear Dynamics.

[10]  Nasser Sadati,et al.  Design of a fractional order PID controller for an AVR using particle swarm optimization , 2009 .

[11]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[12]  Xiao-gen Shao,et al.  Optimization of PID parameters based on genetic algorithm and interval algorithm , 2009, 2009 Chinese Control and Decision Conference.

[13]  K. Miller,et al.  An Introduction to the Fractional Calculus and Fractional Differential Equations , 1993 .

[14]  Samir Ladaci,et al.  Self-tuning fractional order PIλDµ controller based on extremum seeking approach , 2014, Int. J. Autom. Control..

[15]  B. Walczak,et al.  Particle swarm optimization (PSO). A tutorial , 2015 .

[16]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[17]  Igor Podlubny,et al.  Fractional-order systems and PI/sup /spl lambda//D/sup /spl mu//-controllers , 1999 .

[18]  I. Podlubny.  Fractional-order systems and PIλDμ-controllers , 1999, IEEE Trans. Autom. Control..

[19]  Samir Ladaci,et al.  On Fractional Adaptive Control , 2006 .

[20]  Lixiang Li,et al.  Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm , 2012, Expert Syst. Appl..

[21]  R. Reynolds.  AN INTRODUCTION TO CULTURAL ALGORITHMS , 2008 .