Universal Journal of Control and Automation Vol. 2(1), pp. 25 - 31
DOI: 10.13189/ujca.2014.020104
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Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems


Faezeh Farivar 1,*, Mohammad Ali Nekoui 2, Mahdi Aliyari Shoorehdeli 3, Mohammad Teshnehlab 2
1 Department of Mechatronics Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Department of Mechatronics Engineering, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

ABSTRACT

In this paper, evolutionary algorithms are proposed to compute the optimal parameters of Gaussian Radial Basis Adaptive Backstepping Control (GRBABC) for chaotic systems. Generally, parameters are chosen arbitrarily, so in several cases this choice can be tedious. Also, stability cannot be achieved when the parameters are inappropriately chosen. The optimal design problems are to introduce optimization algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO) in order to find the optimal parameters which minimize a cost function defined as an error quadratic function. These methods are applied to two chaotic systems; Duffing Oscillator and Lü systems. Simulation results verify that our proposed algorithms can achieve enhanced tracking performance regarding similar methods.

KEYWORDS
Chaotic Systems, Adaptive Backstepping Control, RBF Neural Network, Genetic Algorithms, Particle Swarm Optimization

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Faezeh Farivar , Mohammad Ali Nekoui , Mahdi Aliyari Shoorehdeli , Mohammad Teshnehlab , "Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems," Universal Journal of Control and Automation, Vol. 2, No. 1, pp. 25 - 31, 2014. DOI: 10.13189/ujca.2014.020104.

(b). APA Format:
Faezeh Farivar , Mohammad Ali Nekoui , Mahdi Aliyari Shoorehdeli , Mohammad Teshnehlab (2014). Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems. Universal Journal of Control and Automation, 2(1), 25 - 31. DOI: 10.13189/ujca.2014.020104.