scholarly journals Projective synchronization and parameter identification of a fractional-order chaotic system

2015 ◽  
Author(s):  
Defu Kong ◽  
Xiaoshan Zhao
Keyword(s):  
Parameter Identification ◽  
Fractional Order ◽  
Chaotic System ◽  
Projective Synchronization

scholarly journals Sliding Mode Control and Modified Generalized Projective Synchronization of a New Fractional-Order Chaotic System

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Junbiao Guan ◽  
Kaihua Wang
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Chaotic System ◽  
Secure Communication ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Control Technique ◽  
Order System ◽  
Mode Control ◽  
The Stability

A new fractional-order chaotic system is addressed in this paper. By applying the continuous frequency distribution theory, the indirect Lyapunov stability of this system is investigated based on sliding mode control technique. The adaptive laws are designed to guarantee the stability of the system with the uncertainty and external disturbance. Moreover, the modified generalized projection synchronization (MGPS) of the fractional-order chaotic systems is discussed based on the stability theory of fractional-order system, which may provide potential applications in secure communication. Finally, some numerical simulations are presented to show the effectiveness of the theoretical results.

Synchronization of Liu Chaotic System with Fractional-Order

2013 ◽  
Vol 336-338 ◽  
pp. 467-470
Author(s):  
Su Hai Huang
Keyword(s):  
Fractional Order ◽  
Chaotic System ◽  
Stability Theory ◽  
Unknown Parameter ◽  
Chaos Synchronization ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Sliding Mode Controller ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller

This paper deals with chaos synchronization of the Liu chaotic system with fractional-order. Based on the fractional-order stability theory, an adaptive sliding mode controller has been constructed to realize projective synchronization of fractional-order Liu chaotic system with unknown parameter. An illustrative simulation result is given to demonstrate the effectiveness of the proposed sliding mode controller.

scholarly journals Adaptive synchronization of a class of fractional order chaotic system with uncertain parameters

2015 ◽  
Vol 11 (6) ◽  
pp. 5306-5316
Author(s):  
De-fu Kong
Keyword(s):  
Parameter Identification ◽  
Numerical Simulations ◽  
Fractional Order ◽  
Chaotic System ◽  
Stability Criterion ◽  
Local Stability ◽  
Adaptive Synchronization ◽  
Uncertain Parameters ◽  
Unknown Parameters ◽  
Control Scheme

In this manuscript, the adaptive synchronization of a class of fractional order chaotic system with uncertain parameters is studied. Firstly, the local stability of the fractional order chaotic system is analyzed using fractional stability criterion. Then, based on the J function criterion, suitable adaptive synchronization controller and parameter identification rules of the unknown parameters are investigated. Finally, the numerical simulations are presented to verify the effectiveness and robustness of the proposed control scheme.

scholarly journals Compound Generalized Function Projective Synchronization for Fractional-Order Chaotic Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Chunde Yang ◽  
Hao Cai ◽  
Ping Zhou
Keyword(s):  
Fractional Order ◽  
Chaotic System ◽  
Chaotic Systems ◽  
Drive System ◽  
Projective Synchronization ◽  
Order System ◽  
Generalized Function ◽  
Function Projective Synchronization ◽  
The Stability ◽  
Generalized Function Projective Synchronization

A modified function projective synchronization for fractional-order chaotic system, called compound generalized function projective synchronization (CGFPS), is proposed theoretically in this paper. There are one scaling-drive system, more than one base-drive system, and one response system in the scheme of CGFPS, and the scaling function matrices come from multidrive systems. The proposed CGFPS technique is based on the stability theory of fractional-order system. Moreover, we achieve the CGFPS between three-driver chaotic systems, that is, the fractional-order Arneodo chaotic system, the fractional-order Chen chaotic system, and the fractional-order Lu chaotic system, and one response chaotic system, that is, the fractional-order Lorenz chaotic system. Numerical experiments are demonstrated to verify the effectiveness of the CGFPS scheme.

Projective Synchronization for Brushless DC Motor (BLDCM) with Fractional-Order

2012 ◽  
Vol 160 ◽  
pp. 327-330
Author(s):  
Su Hai Huang
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Chaotic System ◽  
Dc Motor ◽  
Stability Theorem ◽  
Nonlinear Observer ◽  
Projective Synchronization ◽  
Brushless Dc Motor ◽  
Brushless Dc

Based on orthogonal neural network，a stability theorem[9] and back-stepping approach，a novel adaptive nonlinear observer is designed for a class of fractional-order chaotic system where the nonlinear portion of the structure cannt be evaluated. The projective synchronization of fractional-order BLDCM chaotic system can be achieved.

scholarly journals Parameter Identification of Fractional-Order Discrete Chaotic Systems

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 27 ◽  
Author(s):  
Yuexi Peng ◽  
Kehui Sun ◽  
Shaobo He ◽  
Dong Peng
Keyword(s):  
Parameter Identification ◽  
Fractional Order ◽  
Chaotic System ◽  
Chaos Synchronization ◽  
Chaotic Systems ◽  
Random Noise ◽  
Hidden Attractors ◽  
Noise Interference ◽  
Efficient Performance ◽  
Two Samples

Research on fractional-order discrete chaotic systems has grown in recent years, and chaos synchronization of such systems is a new topic. To address the deficiencies of the extant chaos synchronization methods for fractional-order discrete chaotic systems, we proposed an improved particle swarm optimization algorithm for the parameter identification. Numerical simulations are carried out for the Hénon map, the Cat map, and their fractional-order form, as well as the fractional-order standard iterated map with hidden attractors. The problem of choosing the most appropriate sample size is discussed, and the parameter identification with noise interference is also considered. The experimental results demonstrate that the proposed algorithm has the best performance among the six existing algorithms and that it is effective even with random noise interference. In addition, using two samples offers the most efficient performance for the fractional-order discrete chaotic system, while the integer-order discrete chaotic system only needs one sample.

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