Mathematical Problems in Engineering

Volume 2014, Article ID 142061, 8 pages

http://dx.doi.org/10.1155/2014/142061

## Group Synchronization of Nonlinear Complex Dynamics Networks with Sampled Data

^{1}Mathematics and Physics, Nanyang Institute of Technology, Nanyang, Henan 473004, China^{2}College of Science, North China University of Technology, Beijing 100144, China^{3}School of Economics and Management, Beijing University of Technology, Beijing 100022, China

Received 6 December 2013; Accepted 5 February 2014; Published 24 March 2014

Academic Editor: Huaicheng Yan

Copyright © 2014 Man Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Based on a nonlinear consensus protocol, this paper considers the group synchronization of complex dynamical networks with sampled data. Using the Lyapunov method, the group synchronization of the nonlinear complex networks is analyzed. All the nodes in each group can converge to their own synchronous state asymptotically, if the sampled period satisfies some matrix inequality conditions. Furthermore, the theoretical results are verified by some simulations.

#### 1. Introduction

Because of the wide application of the complex dynamical networks [1–26], the synchronization problem of the complex networks has become a hot topic recently. A lot of researches concentrate on the continuous information transmission; that is, every node can receive or detect neighbor information of the network all the time [1–3, 5–7, 10]. In [1], the authors considered the group synchronization of the nonlinear complex dynamical network via pinning control. A new criterion for the cluster synchronization of coupling networks with time-varying delays was established in continuous time in [2]. Moreover, Liu et al. [3] considered the adaptive synchronization of complex dynamical networks with switching topology by local Lipschitz nonlinearity. In [5], group consensus of multiagent systems with switching topologies and communication delays was studied in the continuous-time case.

However, in many engineering practices, the information communication between nodes could be interrupted at any time due to some factors, such as the unreliability of communication channels and the limitations of the node detection ability. Hence, it is necessary to consider the information transmission under the discrete state [9, 13–16, 18–21, 23, 26]. Information data on the discrete time can also be regarded as sampled data [11, 25]. In [11], the group consensus of linear multiagent systems with sampled-data was considered. Xiao and Wang further considered a discrete-time model with time delays in [13] and analyzed a consensus problem in the existence of the time delay when agents exchanged information between each other. Gao and Wang [16] studied the continuous-time consensus of multiagent systems with sampled data by time-varying topology. In [18], consensus of multiple dynamic agents with sampled information was considered. In [20], average consensus control of networks with sampled data and measurement noises was studied on continuous time. In this paper, we consider the group synchronization of nonlinear complex dynamical network with sampled data.

The rest of this paper is organized as follows. In Section 2, we consider the group synchronization of a complex dynamical network with sampled data and give some assumptions and lemmas. In Section 3, we analyze group synchronization of the proposed network and give the synchronization condition based on linear matrix inequality (LMI). In Section 4, we give the synchronization condition for a special case. In Section 5, the simulations verify the theoretical results. Conclusion is finally summarized in Section 6.

#### 2. Preliminaries and Problem Statement

Consider a complex dynamical network of nodes with sampled data as follows: with where represents the state vectors of the node at time , is continuously differentiable, is coupling strength, and is an inner-coupling matrix, if the node links through its th state with its neighbors ; otherwise . In this protocol, let , and let , where . , , , with . is the neighbors of the node , , where , .

In protocol (2), is the control input: where is an on-off control; if the system is sampling the data, then ; otherwise . If node can get information from node in the same group, then ; otherwise ; if node can get information from node between different groups, then ; otherwise . Thus the coupling configuration matrix can be written as where and represent the coupling configuration of the subgroups, respectively.

Network (1) is said to group synchronization if where the nonlinear function satisfies and , are the synchronous states.

Given a positive real number and a sampled period , we suppose that where shows the discrete time that the node can obtain information from its neighbors and positive integer is a sampled time about the time (), and it satisfies . Under this condition, a linear consensus protocol based on a linear estimation-based sampling period is designed as follows [11]:

Thus, we have

Then the complex dynamical network (1) in every sampled period becomes with

Some assumptions and lemmas are needed.

*Assumption 1 (see [3]). *If each of the nonlinear function in network (1) satisfies the local Lipschitz condition, for any compact set , there exists a positive constant matrix , such that

*Assumption 2 (see [1]). *Assume that protocol (1) satisfies the balance of effectiveness between the subgroups

Lemma 3 (see [1]). *Define , and construct a closed space
**
where and is a constant. For , there exists a constant such that
*

*Lemma 4 (see [1]). Suppose that and are vectors, and in matrix , the following inequality holds:
*

In this paper, means that is a positive (or semipositive, negative, or seminegative) definite matrix; , .

*3. Group Synchronization Analysis in Complex Network with Sampled Data*

*3. Group Synchronization Analysis in Complex Network with Sampled Data*

*In this section, we consider group synchronization problem of the complex networks with sampled data. We have the following theorem.*

*Theorem 5. For network (1) with protocol (12) of nodes, under Assumptions 1-2 and Lemmas 3-4, if , satisfy
then all the nodes in each group can converge to their own synchronous state asymptotically, where
*

*Proof. *Construct a Lyapunov function as follows:
where

Define

Then,

Under Assumption 2, we can know
Under Assumption 1 and Lemma 3, we can have
Then, we can get

Using Lemma 4, we can have
Thus, we can obtain
If
then
Therefore, decreases on an interval , , and . From the above discussion, we have

Then, network (1) with sampled data is group-synchronized under protocol (12).

*4. A Special Case*

*4. A Special Case**In this section, we consider a special case about network (1). For convenience, we let and .*

*Lemma 6 (see [1]). If is a symmetric irreducible matrix with and , then for any matrix with all eigenvalues of the matrix are negative.In networks, when the information transmission is the same between the nodes and , the coupling configuration matrix is symmetric. From Lemma 6, when is symmetric, and are negative. Hence, all of their eigenvalues are strictly negative; one denotes them as
Then, using Lemma 6, we can obtain the following result when the coupling configuration matrix is symmetric.*

*Theorem 7. For network (1) with protocol (12) of nodes, when the coupling configuration matrix is symmetric, under Assumptions 1-2 and Lemmas 3–6, if , satisfy
then all the nodes in each group can converge to their own synchronous state asymptotically, where and .*

The proof of Theorem 7 is similar to that of Theorem 5 and here is omitted.

*5. Simulations*

*5. Simulations*

*In this section, we give some simulation results of the above discussions. For convenience, let , , , for all , , and , . We consider group synchronization of complex dynamical network with sampled data in the time interval . The curves in the graphs are the locations of nodes and the synchronous targets in the network.*

*From Figures 1, 2, and 3, the synchronous states are , . From these figures, we can see that all the nodes in each subgroup can converge to their own synchronous targets. But, in Figures 4, 5, and 6, all of the nodes can asymptotically converge to a synchronous state, when the synchronous state is the only one .*

*In Figures 1 and 4, we simulate synchronization of the network including two subgroups without sampled data. In Figures 2 and 5, we chose as the sampled period. That is, the complex network is sampling at the moment , for . In Figures 3 and 6, we chose as the sampled period; that is, the complex network is sampling at the moment , for .*

*In these simulation results, we can find that the nodes of the system keep their state value in the first time until before the moment , keeping their state value in time until the moment and so on. By comparing, when choosing the sampled data on every period, the rate of convergence of the nodes in the complex dynamics network is slow. Moreover, the bigger the sampled period is, the slower the rate of convergence of the nodes in the complex dynamics network is.*

*6. Conclusion*

*6. Conclusion*

*In this paper, we have investigated the group synchronization problem of a complex dynamical network with sampled data. We prove that the nodes of the network arrive at synchronization in two subgroups if the sampling period satisfies the condition based on the linear matrix inequality (LMI). In addition, we have given some simulation results about the proposed complex network.*

*Conflict of Interests*

*Conflict of Interests*

*The authors declare that there is no conflict of interests regarding the publication of this paper.*

*Acknowledgments*

*Acknowledgments*

*This work was supported by the National Natural Science Foundation of China under Grant no. 61304049, Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR201108055), and Science and Technology Development Plan Project of Beijing Education Commission (nos. KM201310009011 and KM201310009013).*

*References*

*References*

- B. Liu, P. F. Wei, X. F. Wang et al., “Group synchronization of complex networks with nonlinear dynamics via pinning control,” in
*Proceeding of the 32nd Chinese Control Conference*, pp. 235–240, 2013. - S. Wang, H. Yao, S. Zheng, and Y. Xie, “A novel criterion for cluster synchronization of complex dynamical networks with coupling time-varying delays,”
*Communications in Nonlinear Science and Numerical Simulation*, vol. 17, no. 7, pp. 2997–3004, 2012. View at Publisher · View at Google Scholar · View at Scopus - B. Liu, X. L. Wang, Y. P. Gao, G. M. Xie, and H. S. Su, “Adaptive synchronization of complex dynamical networks governed by local lipschitz nonlinearlity on switching topology,”
*Journal of Applied Mathematics*, vol. 2013, Article ID 818242, 7 pages, 2013. View at Publisher · View at Google Scholar - H. Zhang, H. Yan, F. Yang, and Q. Chen, “Quantized control design for impulsive fuzzy networked systems,”
*IEEE Transactions on Fuzzy Systems*, vol. 19, no. 6, pp. 1153–1162, 2011. View at Publisher · View at Google Scholar · View at Scopus - L. Wang, X.-J. Kong, H. Shi, H.-P. Dai, and Y.-X. Sun, “LMI-based criteria for synchronization of complex dynamical networks,”
*Journal of Physics A*, vol. 41, no. 28, Article ID 285102, pp. 1751–8113, 2008. View at Publisher · View at Google Scholar · View at Scopus - H. Su, X. Wang, and Z. Lin, “Flocking of multi-agents with a virtual leader,”
*IEEE Transactions on Automatic Control*, vol. 54, no. 2, pp. 293–307, 2009. View at Publisher · View at Google Scholar · View at Scopus - J. Yu and L. Wang, “Group consensus in multi-agent systems with switching topologies and communication delays,”
*Systems and Control Letters*, vol. 59, no. 6, pp. 340–348, 2010. View at Publisher · View at Google Scholar · View at Scopus - H. Zhang, H. C. Yan, F. W. Yang, and Q. J. Chen, “Distributed average filtering for sensor networks with sensor saturation,”
*IET Control Theory and Applications*, vol. 7, no. 6, pp. 887–893, 2013. View at Google Scholar - H. C. Yan, Z. Z. Su, H. Zhang, and F. W. Yang, “Observer-based ${H}_{\infty}$ control for discrete-time stochastic systems with quantization
and random communication delays,”
*IET Control Theory and Applications*, vol. 7, no. 3, pp. 372–379, 2013. View at Google Scholar - J. Yu and L. Wang, “Group consensus of multi-agent systems with directed information exchange,”
*International Journal of Systems Science*, vol. 2, no. 2, pp. 334–348, 2012. View at Publisher · View at Google Scholar · View at Scopus - Y. J. Yu, M. Yu, J. P. Hu, and B. Liu, “Group consensus of multi-agent systems with sampled data,” in
*Proceeding of the 32nd Chinese Control Conference*, pp. 7168–7172, 2013. - H. C. Yan, H. B. Shi, H. Zhang, and F. W. Yang, “Quantized ${H}_{\infty}$ control for networked delayed systems with communication constraints,”
*Asian Journal of Control*, vol. 15, no. 5, pp. 1468–1476, 2013. View at Google Scholar - F. Xiao and L. Wang, “Consensus protocols for discrete-time multi-agent systems with time-varying delays,”
*Automatica*, vol. 44, no. 10, pp. 2577–2582, 2008. View at Publisher · View at Google Scholar · View at Scopus - J. Yu and L. Wang, “Group consensus of multi-agent systems with directed information exchange,”
*International Journal of Systems Science*, vol. 43, no. 2, pp. 334–348, 2012. View at Publisher · View at Google Scholar · View at Scopus - H. Su, X. Wang, and G. Chen, “A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements,”
*International Journal of Control*, vol. 82, no. 7, pp. 1334–1343, 2009. View at Publisher · View at Google Scholar · View at Scopus - Y. Gao and L. Wang, “Sampled-data based consensus of continuous-time multi-agent systems with time-varying topology,”
*IEEE Transactions on Automatic Control*, vol. 56, no. 5, pp. 1226–1231, 2011. View at Publisher · View at Google Scholar · View at Scopus - H. Zhang, H. Yan, T. Liu, and Q. Chen, “Fuzzy controller design for nonlinear impulsive fuzzy systems with time delay,”
*IEEE Transactions on Fuzzy Systems*, vol. 19, no. 5, pp. 844–856, 2011. View at Publisher · View at Google Scholar · View at Scopus - Y. Gao and L. Wang, “Consensus of multiple dynamic agents with sampled information,”
*IET Control Theory and Applications*, vol. 4, no. 6, pp. 945–956, 2010. View at Publisher · View at Google Scholar · View at Scopus - H. C. Yan, H. Zhang, M. Q. Meng, and H. Shi, “Delay-range-dependent robust ${H}_{\infty}$ filtering for uncertain systems with interval time-varying delays,”
*Asian Journal of Control*, vol. 13, no. 2, pp. 356–360, 2011. View at Publisher · View at Google Scholar · View at Scopus - T. Li and J. Zhang, “Sampled-data based average consensus control for networks of continuous-time integrator agents with measurement noises,” in
*Proceedings of the 26th Chinese Control Conference (CCC '07)*, pp. 716–720, July 2007. View at Publisher · View at Google Scholar · View at Scopus - H. Su, X. Wang, and Z. Lin, “Synchronization of coupled harmonic oscillators in a dynamic proximity network,”
*Automatica*, vol. 45, no. 10, pp. 2286–2291, 2009. View at Publisher · View at Google Scholar · View at Scopus - H. Zhang, Q. Chen, H. Yan, and J. Liu, “Robust ${H}_{\infty}$ filtering for switched stochastic system with missing measurements,”
*IEEE Transactions on Signal Processing*, vol. 57, no. 9, pp. 3466–3474, 2009. View at Publisher · View at Google Scholar · View at Scopus - H. Su, G. Chen, X. Wang, and Z. Lin, “Adaptive second-order consensus of networked mobile agents with nonlinear dynamics,”
*Automatica*, vol. 47, no. 2, pp. 368–375, 2011. View at Publisher · View at Google Scholar · View at Scopus - L. X. Zhang, H. J. Gao, and O. Kaynak, “Network-induced constraints in networked control systems—a survey,”
*IEEE Transactions on Industrial Informatics*, vol. 9, no. 1, pp. 403–416, 2013. View at Google Scholar - H. Liu, G. Xie, and L. Wang, “Necessary and sufficient conditions for solving consensus problems of double-integrator dynamics via sampled control,”
*International Journal of Robust and Nonlinear Control*, vol. 20, no. 15, pp. 1706–1722, 2010. View at Publisher · View at Google Scholar · View at Scopus - H. Su, X. Wang, and G. Chen, “Rendezvous of multiple mobile agents with preserved network connectivity,”
*Systems and Control Letters*, vol. 59, no. 5, pp. 313–322, 2010. View at Publisher · View at Google Scholar · View at Scopus

*
*