Abstract and Applied Analysis

Volume 2013 (2013), Article ID 576290, 8 pages

http://dx.doi.org/10.1155/2013/576290

Research Article

Consensus for Multiagent Systems with Heterogeneous Time-Varying Delays

Beibei Wang and Yuangong Sun

School of Mathematical Sciences, University of Jinan, Jinan, Shandong 250022, China

Received 5 June 2013; Revised 25 October 2013; Accepted 27 October 2013

Academic Editor: Sakthivel Rathinasamy

Copyright © 2013 Beibei Wang and Yuangong Sun. 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.

We apply the linear matrix inequality method to consensus and consensus problems of the single integrator multiagent system with heterogeneous delays in directed networks. To overcome the difficulty caused by heterogeneous time-varying delays, we rewrite the multiagent system into a partially reduced-order system and an integral system. As a result, a particular Lyapunov function is constructed to derive sufficient conditions for consensus of multiagent systems with fixed (switched) topologies. We also apply this method to the consensus of multiagent systems with disturbances and heterogeneous delays. Numerical examples are given to illustrate the theoretical results.

1. Introduction

In recent years, decentralized coordination of multiagent systems has received many researchers' attention in the areas of system control theory, biology, communication, applied mathematics, computer science, and so forth. In cooperative control of multiagent systems, a critical problem is to design appropriate protocols such that multiple agents in a group can reach consensus. So far, by using the matrix theory, the graph theory, the frequency-domain analysis method, the Lyapunov direct method, and so forth, consensus problems for various kinds of multiagent systems have been studied extensively [1–3].

In the field of systems and control theory, the pioneering work was done by Borkar and Varaiya [4] and Tsitsiklis and Athans [5], where the asynchronous consensus problem with an application in distributed decision-making systems was considered. Later, Vicsek et al. [6] proposed a simple but interesting discrete-time model of multiple agents which can be viewed as a special case of a computer model mimicking animal aggregation. Jadbabaie et al. [7] provided a theoretical explanation of the consensus property of the Vicsek model.

For the case of single integrator multiagent systems, Olfati-Saber and Murray [8] discussed the consensus problem for networks of dynamic agents by defining a disagreement function. Ren and Beard [9] established some more relaxable consensus conditions under dynamically changing interaction topologies. Hui and Haddad [10], Liu et al. [11], and Bauso et al. [12] investigated the consensus problem for nonlinear multiagent systems. Tan and Liu [13] studied consensus of networked multiagent systems via the networked predictive control. Li and Zhang [14] gave the necessary and sufficient condition of mean square average-consensus for multiagent systems with noises. Zheng and Wang [15] studied finite-time consensus of heterogenous multiagent systems with and without velocity measurements. The constrained consensus problem for multiagent systems in unbalanced networks was investigated in Lin and Ren [16, 17]. Liu et al. [18] considered the consensus problem for multiagent systems with inherent nonlinear dynamics under directed topologies. A distributed shortest-distance consensus problem under dynamically changing network topologies was studied by Lin and Ren [19]. Li et al. [20] studied the distributed consensus problem of multiagent systems with general continuous-time dynamics for both the case without and with a leader.

When considering communication delays in the feedback, three types of consensus protocols have been analyzed: (i) both the state of the agent and its neighbors are affected by identical delays [21–27]; (ii) communication delays only affect the state received from neighbors of the agent [28–32]; (iii) the state of the agent and its neighbors are affected by heterogeneous delays. Note that (i) and (ii) are special cases of (iii). Compared to the cases of (i) and (ii), consensus for the case of (iii) receives less attention. Under the restricted assumptions that the graph is undirected and the communication delays are constants, Münz et al. [32, 33] investigated the robustness of the third kind of consensus protocols in both identical and nonidentical agent dynamics.

We are here concerned with the third type of consensus protocols, where the heterogeneous delays are time varying, and the involved graph is directed. It seems to us that the frequency-domain analysis method in [32, 33] becomes invalid in this case, and it is difficult to employ the Lyapunov method directly. In this paper, we will apply the linear matrix inequality method to this problem. In [21–25], the linear matrix inequality method has been used successfully in the case of (i). When applying this method to the case of (iii), we have to overcome some difficulties caused by heterogeneous time-varying delays. For this sake, we first skillfully rewrite the multiagent system by virtue of a partially-reduced-order system and an integral system. Then, by defining a particular Lyapunov function based on the above partially-reduced-order system and the integral system, sufficient conditions to consensus are derived in both cases of fixed topology and switching topologies by using the linear matrix inequality method.

We also consider the consensus problem for the single integrator multiagent system with heterogeneous time-varying delays. So far, there are few consensus results for continuous time multiagent systems. Lin et al. [34] studied distributed robust consensus problem in directed networks of agents with identical delays. Ugrinovskii [35] considered a problem of design of distributed robust filters with consensus by using the recent vector dissipativity theory. Sun and Wang [36] investigated the consensus for the double integrator multiagent system with asymmetric delays.

To the best of our knowledge, little has been known about the consensus problem for the single integrator multiagent systems with heterogeneous time-varying delays and directed network topologies. Therefore, another purpose of this paper is to establish consensus criteria in the cases of heterogeneous delays and directed fixed topology (or switching topologies) by using the linear matrix inequality technique.

This paper is structured as follows. The problem statement and the transformation of the multiagent system are summarized in Section 2. In Section 3, sufficient conditions in terms of linear matrix inequalities are established for consensus and consensus of the single integrator multiagent system with heterogeneous delays. These results are also extended to the case of switching topologies. Numerical examples and simulation results are given in Section 4. The paper is concluded in Section 5.

The notation used throughout this paper is fairly standard. means the transpose of the matrix . is an -dimensional identity matrix. is an -dimensional column vector with . We say if is positive definite, where and are symmetric matrices of the same dimensions. We use an asterisk to represent a term that is induced by symmetry and stands for a block-diagonal matrix. denotes the space of square-integrable vector functions over .

2. Preliminaries

Throughout this paper, we denote a weighted digraph by , where is the set of nodes with and node represents the th agent; is the set of edges, an edge of is denoted by an order pair , and if and only if ; is an -dimensional weighted adjacency matrix with . If is an edge of , node is called the parent of node . A directed tree is a directed graph, where every node, except one special node without any parent, which is called the root, has exactly one parent, and the root can be connected to any other nodes through paths. The Laplacian matrix of digraph is defined by and for , .

Consider the following multiagent system with heterogeneous delays:

where , , , are entries of the weighted adjacency matrix , and are different piecewise continuous communication delays satisfying and for , and and are positive constants.

Denote and . If we set for , by the straightforward computation, we get

where the -dimensional matrix and the -dimensional matrix are defined as follows:

Next, we make use of transformation (2) to derive two descriptor systems of the system (1). Rewrite (1) as follows:

Therefore, by the Newton-Leibniz formula, we get a matrix form of (4):

where .

Denote . By transformation (2) and the property of the Laplacian matrix that , we get from (5) the following two descriptor systems:

When the system involves disturbance input, we consider the following multiagent system of the form

where , , and are defined as above and is the disturbance input satisfying .

Denote the th row of the matrix by , , , and . Similar to the above procedure, we can get the following matrix form of (7):

In this paper, say that system (1) achieves consensus asymptotically if

Say that system (7) solves consensus if system (1) achieves consensus asymptotically, and there exists a constant such that

for all nonzero under zero initial condition.

3. Main Results

The following two lemmas can be concluded from [21, 22].

Lemma 1. The digraph has a spanning tree if and only if the matrix is Hurwitz.

Lemma 2. For any continuous vector on for and and any -dimensional matrix , the following inequality holds:

Based on Lemmas 1 and 2 and the preliminaries given in Section 2, we have the following consensus result for system (1).

Theorem 3. If the digraph has a spanning tree, system (1) achieves consensus asymptotically for appropriate constants and which satisfy the following matrix inequality:

where , , , , and , , and are positive-definite matrices of appropriate dimensions.

Proof. First, we see that (12) is equivalent to

where

On the other hand, we show that (13) is feasible if the digraph has a spanning tree. By Lemma 1, we see that there exists a positive-definite matrix such that . Once the positive-definite matrix is determined, it is not difficult to see that for certain positive-definite matrices and . Once the matrices , , and are determined, (13) is valid for sufficiently small and . Therefore, (13) always holds for appropriate matrices , , and and constants , if has a spanning tree.

Next, by the definition of vector , it is sufficient to show that if (13) holds. We construct the Lyapunov function as follows:

where positive constants and and positive-definite matrices , , and satisfy (13). We now consider the derivative of along the trajectory of system (6). For the sake of convenience, set

A straightforward computation yields that

By Lemma 1, (6), and (17), we have that

where and and are defined by (14). From (13) and (18), we have that for . Therefore, we eventually conclude that . This completes the proof of Theorem 3.

The method used in Theorem 3 can also be applied to the consensus problem of system (7).

Theorem 4. For given constants and , if there exist positive-definite matrices , , and of appropriate dimensions and positive constants and such that the following linear matrix inequality holds:

where , , , , and are defined as above, then system (7) solves consensus with .

Proof. Choose the Lyapunov function defined by (15). First, (19) implies that (12) holds. By Theorem 3, system (1) achieves consensus asymptotically. Next, we show that (10) holds with for all nonzero under zero initial condition. In fact, similar to the computation in Theorem 3, we have

which implies that

where ,

By (19), (21), and (22), we have that

Integrating (23) from to under zero initial condition, we get

This completes the proof of Theorem 4.

Remark 5. Similar to the analysis in the proof of Theorem 3, we see that a necessary condition to Theorem 3 is that the digraph has a spanning tree. That is, the linear matrix inequality (19) implies that has a spanning tree.

Remark 6. Unlike most of consensus analysis for multiagent systems, it does not require that for all in the proofs of Theorems 3 and 4. Therefore, even when for some , system (7) can also solve consensus under appropriate conditions.

The method used in this paper can also be extended to the case of switching topology. Consider the following multiagent system with switched topologies and heterogeneous delays:

where , is a switching signal that determines which subsystem is active at time , and , , , are entries of the weighted adjacency matrix . When , we denote the involved digraph by .

Under the reduced-order transformation (2), system (25) can be described by the following two systems:

where and are defined as above and , , and () relative to the digraph are defined as , , and , respectively. Let the Lyapunov function defined by (15) be the common Lyapunov function for the switched system (26). Then, similar to the proof of Theorem 3, we have the following consensus result in the case of switching topology.

Theorem 7. For given constants and , system (25) solves consensus under arbitrary switching, if there exist positive-definite matrices , , and of appropriate dimensions such that the following linear matrix inequality

holds for , where , , , and .

Similarly, for the following switched multiagent system with disturbance input and heterogeneous delays

we have the following consensus result.

Theorem 8. For given constants and , if there exist positive-definite matrices , , and of appropriate dimensions and positive constants and such that the following linear matrix inequality

holds for , where , , , , , and relative to the digraph are defined as , , , , , and in Theorem 4, then system (28) solves consensus with under arbitrary switching.

4. Numerical Examples

Consider the following four digraphs with six nodes shown in Figure 1, where the weights relative to the edges shown by solid lines and dashed lines are and , respectively.

(a)

(b)

(c)

(d)

Figure 1: Four digraphs: (a) , (b) , (c) , and (d) .

When , we have that (12) is feasible for given and . By Theorem 3, system (1) achieves consensus asymptotically. The state trajectory under the stochastic initial condition is shown in Figure 2. In Remark 6, we show that Theorem 3 can also be applied to the extreme case when part of weights is negative. For example, if , we have that (12) is still feasible for given and . Therefore, system (1) also solves consensus asymptotically even when there exist negative weights . The state trajectory under the stochastic initial condition is shown in Figure 3.

Figure 2: State trajectory of system (1) with .

Figure 3: State trajectory of system (1) with .

For the case of disturbance input, assume that and for . Therefore, . For given and , we get from (19) that . By Theorem 4, we have that system (7) solves consensus with . If we let

the trajectory of system (7) under a stochastic initial condition is shown in Figure 4.

Figure 4: State trajectory of system (7) with and given disturbance.

For the case of switching topologies , we have that (27) is feasible for given and . By Theorem 7, system (25) solves consensus under arbitrary switching.

5. Conclusions

In this paper, we first apply the linear matrix inequality method to consensus of the single integrator multiagent system with heterogeneous time-varying delays in directed networks. Unlike the case of identical delays, the multiagent system with heterogeneous delays usually cannot be transformed to a reduced-order system. To overcome such difficulty, we introduce a partially-reduced-order system and an integral system. As a result, the linear matrix inequality method becomes useful in the analysis of consensus and consensus by constructing a particular Lyapunov function. The main results are also extended to the case of switching topologies. Finally, numerical examples and simulation results are given to illustrate the theoretical results.

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant nos. 61174217 and 61374074) and Natural Science Foundation of Shandong Province (Grant nos. ZR2010AL002 and JQ201119).

References

W. Ren and R. W. Beard, Distributed Consensus in Multi-Vehicle Cooperative Control, Springer, London, UK, 2008.

R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007.

W. Ren, R. W. Beard, and E. M. Atkins, “Information consensus in multivehicle cooperative control,” IEEE Control Systems Magazine, vol. 27, no. 2, pp. 71–82, 2007.

V. Borkar and P. P. Varaiya, “Asymptotic agreement in distributed estimation,” IEEE Transactions on Automatic Control, vol. 27, no. 3, pp. 650–655, 1982.

J. N. Tsitsiklis and M. Athans, “Convergence and asymptotic agreement in distributed decision problems,” IEEE Transactions on Automatic Control, vol. 29, no. 1, pp. 42–50, 1984.

T. Vicsek, A. Czirk, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical Review Letters, vol. 75, no. 6, pp. 1226–1229, 1995.

A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Transactions on Automatic Control, vol. 48, no. 6, pp. 988–1001, 2003.

R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1520–1533, 2004.

W. Ren and R. W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 655–661, 2005.

Q. Hui and W. M. Haddad, “Distributed nonlinear control algorithms for network consensus,” Automatica, vol. 44, no. 9, pp. 2375–2381, 2008.

X. Liu, T. Chen, and W. Lu, “Consensus problem in directed networks of multi-agents via nonlinear protocols,” Physics Letters A, vol. 373, no. 35, pp. 3122–3127, 2009.

D. Bauso, L. Giarré, and R. Pesenti, “Non-linear protocols for optimal distributed consensus in networks of dynamic agents,” Systems & Control Letters, vol. 55, no. 11, pp. 918–928, 2006.

C. Tan and G.-P. Liu, “Consensus of networked multi-agent systems via the networked predictive control and relative outputs,” Journal of the Franklin Institute, vol. 349, no. 7, pp. 2343–2356, 2012.

T. Li and J.-F. Zhang, “Mean square average-consensus under measurement noises and fixed topologies: necessary and sufficient conditions,” Automatica, vol. 45, no. 8, pp. 1929–1936, 2009.

Y. Zheng and L. Wang, “Finite-time consensus of heterogeneous multi-agent systems with and without velocity measurements,” Systems & Control Letters, vol. 61, no. 8, pp. 871–878, 2012.

P. Lin and W. Ren, “Constrained consensus in unbalanced networks with communication delays,” IEEE Transactions on Automatic Control, 2013.

P. Lin and W. Ren, “Distributed constrained consensus in the presence of unbalanced switching graphs and communication delays,” IEEE Conference on Decision and Control, vol. 51, pp. 2238–2243, 2012.

K. Liu, G. Xie, W. Ren, and L. Wang, “Consensus for multi-agent systems with inherent nonlinear dynamics under directed topologies,” Systems & Control Letters, vol. 62, no. 2, pp. 152–162, 2013.

P. Lin and W. Ren, “Distributed shortest distance consensus problem in multi-agent systems,” IEEE Conference on Decision and Control, vol. 51, pp. 4696–4701, 2012.

Z. Li, W. Ren, X. Liu, and L. Xie, “Distributed consensus of linear multi-agent systems with adaptive dynamic protocols,” Automatica, vol. 49, no. 7, pp. 1986–1995, 2013.

Y. G. Sun, L. Wang, and G. Xie, “Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays,” Systems & Control Letters, vol. 57, no. 2, pp. 175–183, 2008.

Y. G. Sun and L. Wang, “Consensus of multi-agent systems in directed networks with uniform time-varying delays,” IEEE Transactions on Automatic Control, vol. 54, no. 7, pp. 1607–1613, 2009.

Y. Sun, “Average consensus in networks of dynamic agents with uncertain topologies and time-varying delays,” Journal of the Franklin Institute, vol. 349, no. 3, pp. 1061–1073, 2012.

J. Hu and Y. Hong, “Leader-following coordination of multi-agent systems with coupling time delays,” Physica A, vol. 374, no. 2, pp. 853–863, 2007.

P. Lin and Y. Jia, “Consensus of a class of second-order multi-agent systems with time-delay and jointly-connected topologies,” IEEE Transactions on Automatic Control, vol. 55, no. 3, pp. 778–784, 2010.

P. Lin and Y. Jia, “Multi-agent consensus with diverse time-delays and jointly-connected topologies,” Automatica, vol. 47, no. 4, pp. 848–856, 2011.

P. Lin, K. Qin, H. Zhao, and M. Sun, “A new approach to average consensus problems with multiple time-delays and jointly-connected topologies,” Journal of the Franklin Institute, vol. 349, no. 1, pp. 293–304, 2012.

Y. Gao, J. Ma, M. Zuo, T. Jiang, and J. Du, “Consensus of discrete-time second-order agents with time-varying topology and time-varying delays,” Journal of the Franklin Institute, vol. 349, no. 8, pp. 2598–2608, 2012.

L. Moreau, “Stability of continuous-time distributed consensus algorithms,” in Proceedings of the 43rd IEEE Conference on Decision and Control (CDC '04), pp. 3998–4003, December 2004.

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.

A. Papachristodoulou, A. Jadbabaie, and U. Münz, “Effects of delay in multi-agent consensus and oscillator synchronization,” IEEE Transactions on Automatic Control, vol. 55, no. 6, pp. 1471–1477, 2010.

U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay robustness in consensus problems,” Automatica, vol. 46, no. 8, pp. 1252–1265, 2010.

U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay robustness in non-identical multi-agent systems,” IEEE Transactions on Automatic Control, vol. 57, no. 6, pp. 1597–1603, 2012.

P. Lin, Y. Jia, and L. Li, “Distributed robust consensus control in directed networks of agents with time-delay,” Systems & Control Letters, vol. 57, no. 8, pp. 643–653, 2008.

V. Ugrinovskii, “Distributed robust filtering with consensus of estimates,” Automatica, vol. 47, no. 1, pp. 1–13, 2011.

Y. Sun and L. Wang, “ consensus of second-order multi-agent systems with asymmetric delays,” Systems & Control Letters, vol. 61, no. 8, pp. 857–862, 2012.