Discrete Dynamics in Nature and Society

Volume 2014 (2014), Article ID 627060, 8 pages

http://dx.doi.org/10.1155/2014/627060

Research Article

Pinning Synchronization of One-Sided Lipschitz Complex Networks

Fang Liu,¹ Qiang Song,¹ Jinde Cao,2,3 and Jianquan Lu²

¹School of Information Engineering, Huanghuai University, Henan 463000, China

²Department of Mathematics, Southeast University, Nanjing 210096, China

³Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Received 23 January 2014; Accepted 19 March 2014; Published 13 April 2014

Academic Editor: Zhiqiang Zuo

Copyright © 2014 Fang Liu 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.

This paper studies the pinning synchronization in complex networks with node dynamics satisfying the one-sided Lipschitz condition which is less conservative than the well-known Lipschitz condition. Based on M-matrix theory and Lyapunov functional method, some simple pinning conditions are derived for one-sided Lipschitz complex networks with full-state and partial-state coupling, respectively. A selective pinning scheme is further provided to address the selection of pinned nodes and the design of pinning feedback gains for one-sided Lipschitz complex networks with general topologies. Numerical results are given to illustrate the effectiveness of the theoretical analysis.

1. Introduction

A complex network is composed of a set of coupled dynamical systems where each system updates its state based on the local neighboring information such that the whole network might exhibit collective behaviors under some conditions. Nowadays, many natural and man-made systems in our daily life, such as biological networks, generic networks, electrical power grids, and the World Wide Web, can be described by complex networks.

The synchronization problem for complex networks has attracted much attention from various disciplines over the past decade [1, 2]. By proposing the master stability function, Pecora and Carroll [3] developed a unified approach to study the local synchronization in a network of dynamical systems. Wu [4] showed that the synchronization in a complex network with a sufficiently large coupling strength can always be reached if the interaction digraph of the network contains a directed spanning tree. Lu and Chen [5] presented a systematic framework to investigate the synchronization in linearly coupled complex networks with general topologies. Gómez-Gardeñes et al. [6] deeply studied the effects of coupling strength and network topology on the synchronizability of complex networks.

The synchronization of complex networks is usually achieved by using full-state variables of network nodes which may not always be available in practice. To resolve this difficulty, some control algorithms have been proposed to synchronize complex networks by utilizing the observed states of network nodes. Based on the state observer approach, Jiang et al. [7] formulated a complex network model and then derived some criteria to discuss the local synchronization in the network. Wu and Jiao [8] extended the work of Jiang et al. [7] to address the synchronization in complex networks with asymmetric coupling.

If a complex network cannot achieve synchronization by itself, one can design some appropriate controllers to force the network to synchronize onto a homogenous trajectory. However, for a network consisting of a large number of nodes, the control cost will be very high when the control actions are applied to all network nodes. Fortunately, one can adopt the pinning control strategy [9–20] to achieve synchronization in complex networks by placing control injections onto a subset of network nodes. Current studies have shown that an undirected complex network can be synchronized to some desired state by specifically or randomly pinning some nodes [9–12]. However, due to asymmetric coupling, the pinning control problem of a directed network is much more difficult than that of an undirected network. In recent years, some progress has been made in the pinning synchronization of directed networks. Chen et al. [13] and Lu et al. [14] have shown that a directed network can be pinned to the isolated node by using a minimum number of controllers, even a single controller. More recently, some researchers [18–20] proposed some techniques based on M-matrix theory [21] to study the pinning control problem for networked systems.

It is well-known that each node in complex networks is usually described by a nonlinear dynamical system. In most existing results on pinning control of complex networks, the node dynamics is assumed to satisfy the QUAD condition [12, 13, 15, 18]. Some authors considered the pinning control of complex networks with node dynamics satisfying Lipschitz condition [11, 14] and sector-restriction condition [22, 23]. In this paper, we further consider the pinning synchronization of complex networks composed of a set of nonlinear dynamical systems satisfying the one-sided Lipschitz condition [24–26] which is less conservative than the classical Lipschitz condition.

The main contribution of this paper is threefold. First, the paper studies the pinning control problem for both full-state and partial-state coupled complex networks with one-sided Lipschitz-type node dynamics. To the best of our knowledge, the synchronization in one-sided Lipschitz complex networks has not been addressed up to date. Therefore, the pinning control results for one-sided Lipschitz complex networks in this paper fill in this gap in time. Second, by using the properties of M-matrices, some simple pinning conditions in terms of low-dimensional linear matrix inequalities (LMIs) are established for both full-state and partial-state coupled complex networks. With the derived stability criteria, the pinning control problem of a large-scale network can be reduced to the test of a linear matrix inequality whose dimension is the same as that of a single network node. Third, we discuss the selection of pinned nodes and the design of pinning feedback gains for one-sided Lipschitz complex networks with both directed and undirected topologies based on M-matrix and algebraic graph theories.

The rest of this paper is organized as follows. In Section 2, some mathematical preliminaries are provided. Section 3 formulates the pinning control problem for complex networks with one-sided Lipschitz-type node dynamics. Sections 4 and 5 present some easily verified conditions for reaching pinning control in complex networks with full-state and partial-state coupling, respectively. Section 6 proposes a selective pinning scheme to satisfy the derived pinning conditions. In Section 7, numerical results are given to validate the theoretical analysis. Finally, some concluding remarks are stated in Section 8.

2. Preliminaries

This section provides some mathematical preliminaries to derive the main results of this paper.

2.1. Notations

The notations in this paper are quite standard. Let and represent the real number and complex number sets, respectively. Denote as the real part of a complex number . Let be the -dimensional identity matrix and let be the vector of all ones. For a square matrix , let and with being the th eigenvalue. For a symmetric real matrix , let and be its minimum and maximum eigenvalues, respectively, and write () if is positive definite (positive semidefinite). For a vector , let be its Euclidean norm. Given two matrices and , denote as their Kronecker product [21].

2.2. One-Sided Lipschitz Dynamical System

A nonlinear dynamical system which does not satisfy Lipschitz condition may satisfy the so-called one-sided Lipschitz condition [24, 25].

Definition 1 (see [24]). A nonlinear function is said to satisfy one-sided Lipschitz condition if there exist a positive definite matrix and a constant such that holds for any , where denotes the inner product of two vectors.

Remark 2. The one-sided Lipschitz condition can be rewritten as . The constant is called the one-sided Lipschitz constant. It is worth mentioning that the one-sided Lipschitz constant can be any real number even a nonpositive number [24–26].

Remark 3. It is well-known that a nonlinear function is said to satisfy Lipschitz condition if there exists a positive number such that holds for all . Therefore, one can easily obtain the Lipschitz constant by some simple calculations. However, for a one-sided Lipschitz function, it is quite challenging to find the one-sided Lipschitz constant because one has to design an appropriate structure for the matrix in advance. In Section 7, some detailed examples are given to discuss the determination of the one-sided Lipschitz constant.

Remark 4. From Definition 1 and Remark 3, one can see that a one-sided Lipschitz function automatically satisfies Lipschitz condition, but the converse may be not true.

2.3. M-Matrix Theory

Some properties of M-matrices are important to study the pinning control of complex networks.

Lemma 5 (see [21]). For a nonsingular matrix with , the following statements are equivalent:

(1)is an M-matrix;(2)all entries of are nonnegative;(3)all eigenvalues of have positive real parts; that is, for all ;(4)there exists a positive definite diagonal matrix such that .

3. Model Description and Problem Formulation

Consider a complex network composed of identical coupled nodes, in which each node is a nonlinear dynamical system including linear and nonlinear terms:

where is the state variable of the th node, , is a vector-valued function, is the coupling strength, is the th entry of Laplacian matrix defined as if there is a directed link from node to node () and , otherwise, , is the inner coupling matrix, and is the control input for node .

The leader node (or isolated node) for complex network (1) is given by

where .

For complex network (1), let denote the set of all network nodes, where each node can only access the information of its neighbors. For the isolated node (2) which is not affected by any other node, we call its neighbors pinned nodes and let denote the set of pinned nodes, where . Consider the following pinning control algorithm for complex network (1):

where the pinning feedback gains are defined as follows:

The node dynamics of complex network (1) is assumed to satisfy the one-sided Lipschitz condition.

Assumption 6. Suppose that a positive definite matrix and a constant can be found such that the nonlinear function in complex network (1) satisfies one-sided Lipschitz condition; that is,

4. Pinning Criteria for Network with Full-State Coupling

In this section, we consider the synchronization in complex network (3) with full-state coupling and derive some simple conditions for reaching pinning synchronization in the network.

Letting in complex network (3), we have the following pinning-controlled network:

Remark 7. We see that complex network (6) is full-state coupled with all state variables of network nodes being used to achieve pinning synchronization.

Let . From (2) and (6), we obtain the following error system:

Denote , , and . Rewrite (7) in the matrix form as

where .

Based on M-matrix and algebraic graph theories, Song et al. [18, 19] showed that the quantity plays a fundamental role in analyzing the pinning control of networked systems. Now, we develop some pinning criteria for complex network (6).

Theorem 8. Suppose that there exists a positive definite matrix such that Assumption 6 holds and the following condition is satisfied:

where is the one-sided Lipschitz constant and is a positive constant subject to

where .

Then, the pinning-controlled network (6) globally asymptotically synchronizes to leader node (2); that is, , , as , for any initial condition.

Proof. Let be the th eigenvalue of . It is easy to verify that is the th eigenvalue of . It follows from condition (10) that holds for all . Then, by the definition of and Lemma 5, we know that is an M-matrix and there exists a positive definite diagonal matrix such that

Construct the following Lyapunov function candidate:

where satisfies LMI condition (9).

By Assumption 6 and the properties of Kronecker product [21], the time derivative of along the trajectory of error system (8) yields

It follows from (9), (11), and (13) that holds for all . Hence, the error system (8) is globally asymptotically stable at the origin. Then, complex network (6) globally asymptotically synchronizes to leader node (2).

Remark 9. From condition (10), we see that the quantity is important to study the pinning synchronization in network (6). To ensure , Song et al. [18, 19] have shown that the leader node should have a directed path to every other network node. Section 6 will provide more details to discuss the selection of pinned nodes and the design of pinning feedback gains.

Remark 10. Note that the dimension of LMI (9) is the same as that of a single network node. By choosing the constant according to condition (10), one can solve LMI (9) to find feasible solution of by using some toolboxes such as YALMIP [27].

In Theorem 8, the interaction digraph of complex network is assumed to be general. When is undirected, it is easy to obtain and one has the following result from Theorem 8.

Corollary 11. Assume that is symmetric. There exists a positive definite matrix such that Assumption 6 holds and

where . Then, complex network (6) globally asymptotically synchronizes to leader node (2).

Proof. Obviously, is symmetric with all eigenvalues being real numbers when . By Lemma 5, we know that matrix is positive definite if .

Take the Lyapunov function candidate as follows:

where satisfies LMI condition (14).

In view of the proof of Theorem 8, the time derivative of along the trajectory of error system (8) satisfies

where the second inequality is obtained by using .

From (14) and (16), we can show that the pinning-controlled complex network (6) globally asymptotically synchronizes to leader node (2) by using Lyapunov stability theory.

Remark 12. When , the topology of network (3) is undirected. Then, the pinned nodes can be randomly or specifically chosen, and one can first pin the most highly connected nodes to achieve larger [9, 10].

5. Pinning Criteria for Network with Partial-State Coupling

In the previous section, complex network (6) is a full-state coupled network. However, in many practical cases, the full states of network nodes may not always be available. By utilizing the output states of network nodes, this section derives some pinning criteria for partial-state coupled complex networks.

For complex network (3), assume that a matrix can be freely chosen such that the matrix pair is detectable. For complex network (3), let , where is a positive definite matrix to be designed. Consider the following pinning-controlled network:

Remark 13. Let , be the output states of network nodes and let be the output-feedback gain matrix such that is Hurwitz. Then, complex network (17) can be rewritten as

Note that the output states are actually adopted to reach pinning synchronization in complex network (17), which means that network (17) is partial-state coupled rather than full-state coupled.

Remark 14. If the matrix pair is detectable, one can always find a matrix to ensure that is Hurwitz. Moreover, there always exist a positive definite matrix and a scalar such that [28].

Theorem 15. Suppose that there exists a positive definite matrix such that Assumption 6 and the following condition hold:

where is a positive constant satisfying

Then, the pinning-controlled network (17) globally asymptotically synchronizes to leader node (2).

Proof. Following the similar line in the proof of Theorem 8, from condition (20), we can show that is an M-matrix and there exists a positive definite diagonal matrix such that

From (2) and (17), we obtain the following error system:

Consider the following Lyapunov function candidate:

where satisfies Assumption 6 and condition (19).

Calculate the time derivative of along the trajectory of the error system (22) as follows:

Obviously, is positive semidefinite; that is, . Then, it follows from (19), (21), and (24) that holds for all . Hence, error system (22) is globally asymptotically stable at the origin, which indicates that network (17) globally asymptotically synchronizes to leader node (2).

Remark 16. Note that the dimension of LMI (19) is the same as that of a single network node.

Remark 17. In Remark 14, we have pointed out that one can always find a positive definite matrix and a scalar such that if the matrix pair is detectable. To utilize this important result related to system detectability, we intentionally introduce the parameter in Theorem 15 as a transitional variable to derive condition (19). Treating as a scalar matrix, we can solve LMIs (19) and (20) to obtain appropriate parameters and .

Corollary 18. Suppose that Assumption 6 holds and is symmetric. There exists a positive definite matrix such that

where . Then, the complex network (17) globally asymptotically synchronizes to leader node (2).

Proof. Take the following Lyapunov function candidate as

where satisfies LMI condition (25).

Then, the time derivative of along the trajectory of the error system (22) is obtained as follows:

By (25) and (27), we can show that complex network (17) globally asymptotically synchronizes to leader node (2).

6. Selective Pinning Scheme

In this section, we discuss the selection of pinned nodes and the design of pinning feedback gains. A selective pinning scheme for complex networks with general topologies is proposed to satisfy the pinning conditions in Theorems 8 and 15.

For complex network (1), let denote the interaction graph of the network and suppose that is the multiplicity of the zero eigenvalue of the Laplacian matrix . Song et al. [18, 19] showed that the digraph of the network can be partitioned into components with each component having a directed tree. To ensure that holds, at least one root node in each component should be pinned such that the leader node (2) has a directed path to every other network node. Moreover, Song et al. [19] have shown that monotonically increases with respect to the number of pinned nodes or pinning feedback gains. However, always holds even if the pinning feedback gains are sufficiently large. Therefore, it is better to take relatively lower pinning feedback gains. For directed complex networks, Song and Cao [15] pointed out that the nodes whose out-degrees are bigger than their in-degrees should be chosen as pinned candidates.

Now, we present the following selective pinning scheme for complex network (1) such that the conditions in Theorem 8 (or Theorem 15) can be satisfied for reaching pinning synchronization.

(1)Partition the digraph of complex network (1) into components where each component contains a directed tree. Initialize with root nodes of these components.(2)Rearrange the remaining nodes in descending order according to the differences of their out-degrees and in-degrees and choose appropriate pinning feedback gains.(3)Solve LMI (9) (or LMIs (19) and (20)). If no feasible solution is found, continually add more network nodes to or increase pinning feedback gains until the conditions in Theorem 8 (or Theorem 15) are satisfied.

7. Numerical Examples

In this section, some numerical results are provided to illustrate the effectiveness of our theoretical analysis. A digraph with ten nodes is shown in Figure 1, in which the element of the Laplacian matrix is taken as if there is a directed link from node to node .

Figure 1: A digraph with ten nodes.

Note that the digraph in Figure 1 has a directed tree with node 1 being the unique root. According to the selective pinning scheme in Section 6, node 1 should be pinned. Let be the coupling strength and let be the pinning feedback gain. If node 1 is pinned, we have

Consider a complex network with one-sided Lipschitz-type node dynamics in the form of (1) where

and the nonlinear function is given by

It is not difficult to check that is not a Lipschitz-type function. Now, we use some techniques in [25] to show that satisfies one-sided Lipschitz condition. For any , , and , applying the well-known mean-value theorem yields [25]

where . Hence, is a one-sided Lipschitz function with .

Case 1 (full-state coupling). Taking , we solve LMI (9) by using YALMIP toolbox and obtain

Applying pinning control action to node with algorithm (6), we depict the time variations of network states in Figure 2. Note that the network is successfully pinned to a homogenous state.

Figure 2: State evolutions of complex network with full-state coupling.

Case 2 (partial-state coupling). Choosing , it is easy to verify that the matrix pair is detectable. The following parameters are obtained to satisfy conditions (19) and (20) of Theorem 15:

Using algorithm (17), we apply pinning control to the network by utilizing the output states of the network. The time variations of network states are shown in Figure 3, from which it is easy to see that the network achieves pinning synchronization.

Figure 3: State evolutions of complex network with partial-state coupling.

8. Conclusions

In this paper, the pinning synchronization problem for one-sided Lipschitz complex networks has been investigated by using M-matrix and algebraic graph theories. Some simple pinning criteria in terms of low-dimensional linear matrix inequalities have been established for full-state and partial-state coupled complex networks, respectively. In particular, the output states of network nodes are utilized to implement the distributed pinning control algorithm. A selective pinning scheme has been proposed to satisfy the derived pinning conditions for one-sided Lipschitz complex networks with general topologies. In the near future, it would be of interest to study the pinning control problem for one-sided Lipschitz-type complex networks with dynamically switching topologies and time delays.

Conflict of Interests

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

Acknowledgments

This work was jointly supported by the National Science Foundation of China under Grants 61273218, 61304172, 61272530, and 61175119 and the Natural Science Foundation of Henan Province of China under Grants 122102210027, 122300410220 and 12B480005.

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