Mathematical Problems in Engineering

Volume 2014 (2014), Article ID 676230, 6 pages

http://dx.doi.org/10.1155/2014/676230

## Consensus of Multiagent Networks with Intermittent Interaction and Directed Topology

Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing 400067, China

Received 24 May 2014; Revised 22 July 2014; Accepted 22 July 2014; Published 12 August 2014

Academic Editor: Chuandong Li

Copyright © 2014 Li Xiao. 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

Intermittent interaction control is introduced to solve the consensus problem for second-order multiagent networks due to the limited sensing abilities and environmental changes periodically. And, we get some sufficient conditions for the agents to reach consensus with linear protocol from the theoretical findings by using the Lyapunov control approach. Finally, the validity of the theoretical results is validated through the numerical example.

#### 1. Introduction

The problem of coordinating the motion of multiagent networks has attracted increasing attention. Research on multiagent coordinated control problems not only helps in better understanding the general mechanisms and interconnection rules of natural collective phenomena but also carries out benefits in many practical applications of networked cyber-physical systems, such as tracking [1], flocking [2, 3], and formation [4]. Consensus, along with stability [5] and bifurcation [6], is a fundamental phenomenon in nature [7]. Roughly speaking, consensus means that all agents in network will converge to some common state by negotiating with their neighbors. A consensus algorithm is an interaction rule on how agents update their states.

To realize consensus, many effective approaches were proposed [8–10]. Since the network can be regarded as a graph, the issues can be depicted by the graph theory. The recent approaches concentrate on matrix analysis [11], convex analysis [12, 13], and graph theory [14]. The concept of spanning tree especially is widely used to describe the communicability between agents in networks that can guarantee the consensus [15]. For more consensus problems, the reader may refer to [16–21] and the references therein.

As we know, sometimes only the intermittent states of its neighbors can be obtained by the agents to the transmission capacity, communication cost, sensing abilities, and the environmental changes. To decrease the control cost, only the intermittent states of its neighbors are obtained [22]. This is mainly because such networks are constrained by the following operational characteristics: (i) they may not have a centralized entity for facilitating computation, communication, and timesynchronization, (ii) the network topology may not be completely known to the nodes of the network, and (iii) in the case of sensor networks, the computational power and energy resources may be very limited. Inspired by the above consideration, the goal in this setting is to design algorithms by exploiting partial state sampling at each node; it is possible to reduce the amount of data which needs to be transmitted in networks, thereby saving bandwidth and energy, extending the network lifetime, and reducing latency. Also, the linear local interaction protocol can guarantee the linear nature of distributed multiagent networks in real world and linear algorithm is simple and easy to implement so as to be widely used in practical engineering especially in the limited transmission environment. Using the Lyapunov control approach, some sententious conditions are obtained in this paper for reaching consensus in multiagent networks.

The rest of this paper is organized as follows. In Section 2, some preliminaries on the graph theory and the model formulation are given. The main results are established in Section 3. In Section 4, a numerical example is simulated to verify the theoretical analysis. Concise conclusions are finally drawn in Section 5.

#### 2. Preliminaries and Model

##### 2.1. Graph Theory

In this subsection, some basic concepts and result of algebraic grapy theory are introduced. Suppose that information exchange among agents in multiagent networks can be modeled by an interaction digraph.

Let denote a directed graph with the set of nodes , where represents the edge set and is the adjacency matrix with nonnegative elements . A directed edge in the network is denoted by the ordered pair of nodes , where is the receiver and is the sender, which means that node can receive information from node . We always assume that there is no self-loop in network . An adjacency matrix of a directed graph can be defined such that is a nonnegative element if , while if . The set of neighbors of node is denoted by . A sequence of edges of the form composes a directed path beginning with and ending with in the directed graph with distinct nodes , , which means the node is reachable from node . A directed graph is strongly connected if for any distinct nodes and , there exists a directed path from node to node . A directed graph has a directed spanning tree if there exists at least one node called root which has a directed path to all the other nodes [16]. Let (generally nonsymmetrical) Laplacian matrix associated with directed network be defined by which ensure the diffusion property . Suppose is irreducible. Then, and there is a positive vector satisfying and . In addition, there exists a positive definite diagonal matrix such that is symmetric and for all [18].

For simplicity, some mathematical notations are used throughout this paper. denotes the identity (zero) matrix with dimensions. Let be the vector with all elements being . is the -dimensional real vector space. The notation denotes the Kronecker product.

##### 2.2. Model Description

The discretization process of a continuous-time system cannot entirely preserve the dynamics of the continuous-time part even small sampling period is adopted. So, we consider the following second-order multiagent networks of agents in [19] with intermittent measurements. The th agent in the directed network is governed by double-integrator dynamics where and are the position and velocity states of the th agent, respectively. denotes the coupling strengths. denotes the intermittent control as follows: where is the control period and is called the control width.

Equivalently, model (2) can be rewritten as follows:

In this paper, our goal is to design suitable , such that the network reaches consensus. In the following we present the following lemma and definitions.

Lemma 1 (see [23]). *Suppose that is positive definite and is symmetric. Then , and the following inequality holds:
*

*Definition 2 (see [18]). *Let , , and be defined as in Section 2.1. For a strongly connected network with Laplacian matrix , let

*Definition 3. *Periodic intermittent consensus in the second-order multiagent networks (2) is said to be achieved if, for any initial conditions,

#### 3. Main Results

In this section, we will focus on consensus analysis of second-order multiagent networks via intermittent control in the strongly connected networks, simply for that a matrix is irreducible if and only if its corresponding system is strongly connected [24].

Let , represent the average position and velocity of agent. Naturally, and represent the position and velocity vectors relative to the average position and velocity of the agents in network. Then the error dynamical system can be rewritten in a compact matrix form as where .

Theorem 4. *Suppose that the agent network is strongly connected; then the linear consensus in the multiagent networks (2) via periodic intermittent interaction is achieved if the following conditions are satisfied:*(1)*,*(2)*, , ,**where
*

*Proof. *The potential Lyapunov function is defined to be
where . Computing by the Definition 2, one obtains
where , .

is equivalent to and by Schur Complement Lemma. And it is clear that ; thus . So and if and only if .

Because the control gain works intermittently by the control period and the control width, the consensus is discussed in the two different intervals, respectively.

Firstly, for and , taking the derivative of along the trajectories of (8), it can be obtained that
In addition,
Thus, one obtains that

Therefore, from (12) to (14), one obtains
where .

Using in condition (2), is a negative-definite matrix.

Thus,
And, on the other hand,
Consequently,
Thus, we can obtain

Then let us consider the period . For , we can derive
where
Then,
Based on the above, one obtains
where .

From Lemma 1, we obtain
We set , so as to obtain
Now, we can obtain from (19) and (25) that
It is clear that there is a constant satisfying for any . Thus we get that for ,
and for ,
Hence, let , and we can conclude the following from the above analysis:
which means that the states of agents can achieve consensus. The proof is complete.

#### 4. Numerical Simulations

A multiagent network of four agents is considered as the simulation example. The multiagent network topology is described by a directed network shown in Figure 1. It can be seen that the network is strongly connected.

Let and . With simple calculations, we obtain the , , and . From condition (3), we obtain . So if we set and , second-order consensus can be achieved in system (2). The initial position and velocity values of agents are , , , , , , , and , respectively. Figure 2 shows the linear consensus of position and velocity states of four agents with intermittent control.

#### 5. Conclusions

In this paper, we have considered the linear consensus of multiagent networks with periodic intermittent interaction and directed topology. We choose to show the consensus with linear local interaction protocols, partly for simplifying the problem. On the other hand, it is simple and easy to implement so as to be widely used in practical engineering. The tools from algebraic graph theory, matrix theory, and Lyapunov control approach have been adopted. It is shown that the consensus is determined commonly by the general algebraic connectivity, control period, and control width. And the states of agents converge exponentially.

#### Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

The author sincerely thanks Junjie Bao (Department of Mathematics and Information Engineering, Chongqing University of Education) for his helpful work that led to the present version. And the work was supported by the Foundation Project of CQCSTC (no. cstc2014jcyjA40041), the National Natural Science Foundation of China (no. 60973114), and the Foundation of Chongqing University of Education (no. KY201318B).

#### References

- G. Hu, “Robust consensus tracking of a class of second-order multi-agent dynamic systems,”
*Systems and Control Letters*, vol. 61, no. 1, pp. 134–142, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus - H. G. Tanner, A. Jadbabaie, and G. J. Pappas, “Flocking in fixed and switching networks,”
*IEEE Transacitons on Automatic Control*, vol. 52, no. 5, pp. 863–868, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - H. Zhang, C. Zhai, and Z. Chen, “A general alignment repulsion algorithm for flocking of multi-agent systems,”
*IEEE Transactions on Automatic Control*, vol. 56, no. 2, pp. 430–435, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - P. Lin and Y. Jia, “Distributed rotating formation control of multi-agent systems,”
*Systems & Control Letters*, vol. 59, no. 10, pp. 587–595, 2010. View at Publisher · View at Google Scholar · View at Scopus - C. Li, C. Li, X. Liao, and T. Huang, “Impulsive effects on stability of high-order BAM neural networks with time delays,”
*Neurocomputing*, vol. 74, no. 10, pp. 1541–1550, 2011. View at Publisher · View at Google Scholar · View at Scopus - X. He, C. Li, and Y. Shu, “Bogdanov-Takens bifurcation in a single inertial neuron model with delay,”
*Neurocomputing*, vol. 89, pp. 193–201, 2012. View at Publisher · View at Google Scholar · View at Scopus - C. W. Wu and L. O. Chua, “Synchronization in an array of linearly coupled dynamical systems,”
*IEEE Transactions on Circuits and Systems*, vol. 42, no. 8, pp. 430–447, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - J. Hu and Y. Hong, “Leader-following coordination of multi-agent systems with coupling time delays,”
*Physica A: Statistical Mechanics and its Applications*, vol. 374, no. 2, pp. 853–863, 2007. View at Publisher · View at Google Scholar · View at Scopus - W. Yu, G. Chen, Z. Wang, and W. Yang, “Distributed consensus filtering in sensor networks,”
*IEEE Transactions on Systems, Man, and Cybernetics B*, vol. 39, no. 6, pp. 1568–1577, 2009. View at Publisher · View at Google Scholar · View at Scopus - A. K. Das, R. Fierro, V. Kumar, J. P. Ostrowski, J. Spletzer, and C. J. Taylor, “A vision-based formation control framework,”
*IEEE Transactions on Robotics and Automation*, vol. 18, no. 5, pp. 813–825, 2002. View at Publisher · View at Google Scholar · View at Scopus - Y. Chen, J. Lü, F. Han, and X. Yu, “On the cluster consensus of discrete-time multi-agent systems,”
*Systems and Control Letters*, vol. 60, no. 7, pp. 517–523, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus - H. Wang, X. Liao, T. Huang, and C. Li, “Cooperative distributed optimization in multi-agent networks with delays,”
*IEEE Transactions on Systems, Man, and Cybernetics: Systems*, no. 99, p. 1, 2014. View at Publisher · View at Google Scholar - L. Moreau, “Stability of multiagent systems with time-dependent communication links,”
*IEEE Transactions on Automatic Control*, vol. 50, no. 2, pp. 169–182, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - H. Wang, X. Liao, T. Huang, and C. Li, “Improved weighted average prediction for multi-agent networks,”
*Circuits, Systems and Signal Processing*, vol. 33, no. 6, pp. 1721–1736, 2014. View at Publisher · View at Google Scholar - B. Liu, W. Lu, and T. Chen, “Consensus in networks of multiagents with switching topologies modeled as adapted stochastic processes,”
*SIAM Journal on Control and Optimization*, vol. 49, no. 1, pp. 227–253, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - H. Li, X. Liao, T. Dong, and L. Xiao, “Second-order consensus seeking in directed networks of multi-agent dynamical systems via generalized linear local interaction protocols,”
*Nonlinear Dynamics*, vol. 70, no. 3, pp. 2213–2226, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - L. Xiao and X. Liao, “Periodic intermittent consensus of second-order agents networks with nonlinear dynamics,”
*International Journal of Control, Automation and Systems*, vol. 12, no. 1, pp. 23–28, 2014. View at Publisher · View at Google Scholar - W. Yu, G. Chen, M. Cao, and J. Kurths, “Second-Order consensus for multiagent systems with directed topologies and nonlinear dynamics,”
*IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics*, vol. 40, no. 3, pp. 881–891, 2010. View at Publisher · View at Google Scholar · View at Scopus - S. Wang and D. Xie, “Consensus of second-order multi-agent systems via sampled control: undirected fixed topology case,”
*IET Control Theory & Applications*, vol. 6, no. 7, pp. 893–899, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - F. Xiao and L. Wang, “Asynchronous consensus in continuous-time multi-agent systems with switching topology and time-varying delays,”
*IEEE Transactions on Automatic Control*, vol. 53, no. 8, pp. 1804–1816, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - Y. Zhang and Y. P. Tian, “Consentability and protocol design of multi-agent systems with stochastic switching topology,”
*Automatica*, vol. 45, no. 5, pp. 1195–1201, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - J. Huang, C. Li, and X. He, “Stabilization of a memristor-based chaotic system by intermittent control and fuzzy processing,”
*International Journal of Control, Automation and Systems*, vol. 11, no. 3, pp. 643–647, 2013. View at Publisher · View at Google Scholar · View at Scopus - S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan,
*Linear Matrix Inequalities in System and Control Theory*, vol. 15, Society for Industrial Mathematics, 1994. View at Publisher · View at Google Scholar · View at MathSciNet - R. A. Horn and C. R. Johnson,
*Matrix Analysis*, Cambridge University Press, 1985. View at Publisher · View at Google Scholar · View at MathSciNet