A novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term and its synchronization

被引:9
|
作者
Sahab, Ali Reza [1 ]
Ziabari, Masoud Taleb [2 ]
Modabbernia, Mohammad Reza [3 ]
机构
[1] Islamic Azad Univ, Fac Engn, Elect Engn Grp, Lahijan Branch, Lahijan, Iran
[2] Ahrar Univ, Fac Engn, Comp Engn Grp, Rasht, Iran
[3] Tech & Vocat Univ, Elect Engn Dept, Rasht Branch, Rasht, Iran
关键词
novel fractional-order hyperchaotic system; generalized backstepping method; synchronization; genetic algorithm; cuckoo optimization algorithm; particle swarm optimization algorithm; imperialist competitive algorithm; cost function; AUTONOMOUS CHAOTIC SYSTEM; PROJECTIVE SYNCHRONIZATION; DYNAMICS;
D O I
10.1186/1687-1847-2012-194
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The dynamics of fractional-order systems have attracted increasing attention in recent years. In this paper a novel fractional-order hyperchaotic system with a quadratic exponential nonlinear term is proposed and the synchronization of a new fractional-order hyperchaotic system is discussed. The proposed system is also shown to exhibit hyperchaos for orders 0.95. Based on the stability theory of fractional-order systems, the generalized backstepping method (GBM) is implemented to give the approximate solution for the fractional-order error system of the two new fractional-order hyperchaotic systems. This method is called GBM because of its similarity to backstepping method and more applications in systems than it. Generalized backstepping method approach consists of parameters which accept positive values. The system responses differently for each value. It is necessary to select proper parameters to obtain a good response because the improper selection of parameters leads to inappropriate responses or even may lead to instability of the system. Genetic algorithm (GA), cuckoo optimization algorithm (COA), particle swarm optimization algorithm (PSO) and imperialist competitive algorithm (ICA) are used to compute the optimal parameters for the generalized backstepping controller. These algorithms can select appropriate and optimal values for the parameters. These minimize the cost function, so the optimal values for the parameters will be found. The selected cost function is defined to minimize the least square errors. The cost function enforces the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.
引用
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页数:21
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