Adaptive learning control synchronization for unknown time-varying complex dynamical networks with prescribed performance

2021 ◽  
Author(s):  
Aili Fan ◽  
Junmin Li
Keyword(s):  
Adaptive Learning ◽  
Learning Control ◽  
Complex Dynamical Networks ◽  
Time Varying ◽  
Dynamical Networks ◽  
Prescribed Performance ◽  
Adaptive Learning Control

scholarly journals Adaptive hybrid function projective synchronization of chaotic systems with fully unknown periodical time-varying parameters

2019 ◽  
Vol 18 (1) ◽  
pp. 112-128
Author(s):  
Jinsheng Xing
Keyword(s):  
Adaptive Learning ◽  
Chaotic Systems ◽  
Learning Control ◽  
Projective Synchronization ◽  
Time Varying ◽  
Hybrid Functional ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Adaptive Learning Control ◽  
Different Chaotic Systems

In this paper, an adaptive learning control approach is presented for the hybrid functional projective synchronization (HFPS) of different chaotic systems with fully unknown periodical time-varying parameters. Differential-difference hybrid parametric learning laws and an adaptive learning control law are constructed via the Lyapunov–Krasovskii functional stability theory, which make the states of two different chaotic systems asymptotically synchronized in the sense of mean square norm. Moreover, the boundedness of the parameter estimates are also obtained. The Lorenz system and Chen system are illustrated to show the effectiveness of the hybrid functional projective synchronization scheme.

scholarly journals Adaptive Synchronization of Nonlinearly Parameterized Complex Dynamical Networks with Unknown Time-Varying Parameters

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tengfei Wang ◽  
Junmin Li ◽  
Shu Tang
Keyword(s):  
Adaptive Learning ◽  
Complex Dynamical Networks ◽  
Adaptive Synchronization ◽  
Time Varying ◽  
Error Norm ◽  
Dynamical Networks ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Nonlinearly Parameterized ◽  
Composite Energy

A new adaptive learning control approach is proposed for a class of nonlinearly parameterized complex dynamical networks with unknown time-varying parameters. By using the parameter separation and reparameterization technique, the adaptive learning laws of periodically time-varying and constant parameters and an adaptive control strategy are designed to ensure the asymptotic convergence of the synchronization error in the sense of square error norm. Then, a sufficient condition of the synchronization is given by constructing a composite energy function. Finally, an example of the complex network is used to verify the effectiveness of proposed approach.

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