Synchronization of complex networks with nonidentical nodes via discrete-time neural inverse optimal pinning control

2018 ◽  
Author(s):  
Carlos J. Vega ◽  
Edgar N. Sanchez ◽  
Oscar J. Suarez
Keyword(s):  
Complex Networks ◽  
Discrete Time ◽  
Pinning Control ◽  
Nonidentical Nodes

scholarly journals Cluster Synchronization for Linearly Coupled Complex Networks with Identical and Nonidentical Nodes

2012 ◽  
Vol 2012 ◽  
pp. 1-17
Author(s):  
Yi Zhao ◽  
Jianwen Feng ◽  
Jingyi Wang
Keyword(s):  
Lyapunov Function ◽  
Complex Networks ◽  
Sufficient Conditions ◽  
Pinning Control ◽  
Cluster Synchronization ◽  
Adaptive Feedback ◽  
Numerical Examples ◽  
Control Schemes ◽  
Nonidentical Nodes ◽  
Theoretical Results

The cluster synchronization of linearly coupled complex networks with identical and nonidentical nodes is studied. Without assuming symmetry, we proved that these linearly coupled complex networks could achieve cluster synchronization under certain pinning control schemes. Sufficient conditions guaranteeing cluster synchronization for any initial values are derived by using Lyapunov function methods. Moreover, the adaptive feedback algorithms are proposed to adjust the control strength. Several numerical examples are given to illustrate our theoretical results.

scholarly journals Learning Impulsive Pinning Control of Complex Networks

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2436
Author(s):  
Alma Y. Alanis ◽  
Daniel Ríos-Rivera ◽  
Edgar N. Sanchez ◽  
Oscar D. Sanchez
Keyword(s):  
Neural Network ◽  
Complex Networks ◽  
Complex Network ◽  
Discrete Time ◽  
Pinning Control ◽  
Rayleigh Quotient ◽  
Control Input ◽  
Linear Connections ◽  
Algebra Approach ◽  
Unknown Node

In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors.

scholarly journals Partial Component Consensus of Discrete-Time Multiagent Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Wenjun Hu ◽  
Gang Zhang ◽  
Zhongjun Ma ◽  
Binbin Wu
Keyword(s):  
Multiagent Systems ◽  
Discrete Time ◽  
Matrix Theory ◽  
Pinning Control ◽  
Directed Network ◽  
Strong Function ◽  
Partial Stability ◽  
Partial Component ◽  
The Matrix ◽  
Error System

The multiagent system has the advantages of simple structure, strong function, and cost saving, which has received wide attention from different fields. Consensus is the most basic problem in multiagent systems. In this paper, firstly, the problem of partial component consensus in the first-order linear discrete-time multiagent systems with the directed network topology is discussed. Via designing an appropriate pinning control protocol, the corresponding error system is analyzed by using the matrix theory and the partial stability theory. Secondly, a sufficient condition is given to realize partial component consensus in multiagent systems. Finally, the numerical simulations are given to illustrate the theoretical results.

Synchronization for discrete-time complex networks with probabilistic time delays

2019 ◽  
Vol 525 ◽  
pp. 1088-1101 ◽  
Author(s):  
Ranran Cheng ◽  
Mingshu Peng ◽  
Jinchen Yu ◽  
Haifen Li
Keyword(s):  
Complex Networks ◽  
Discrete Time ◽  
Time Delays
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