Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 505434 | 8 pages | https://doi.org/10.1155/2013/505434

Second-Order Leader-Following Consensus of Multiagent Systems with Time Delays

Academic Editor: Stefano Lenci
Received18 Apr 2013
Accepted02 Nov 2013
Published18 Nov 2013

Abstract

This paper is concerned with a leader-following consensus problem of second-order multiagent systems with a constant acceleration leader and time-varying delays. At first, a distributed control protocol for every agent to track the leader is proposed; then by utilizing the Lyapunov-Razumikhin function, the convergence analysis under both fixed and switching interconnection topologies is investigated. For the case of fixed topology, a sufficient and necessary condition is obtained, and for the case of switching topology, a sufficient condition is derived under some assumptions. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

1. Introduction

Recent years have witnessed dramatic advances concerned with distributed coordination of multiple agents due to rapid developments of computer science and communication technologies. As an important issue of distributed coordination, the consensus problem has attracted more and more attention from researchers in various fields [15]. A special case of the issue is known as the leader-following consensus problems and has been investigated from different perspectives. Hong et al. in [6], for instance, designed distributed observers to track an active leader for second-order continuous multi-agents systems. Also, Tang et al. studied the leader-following consensus problem via sampled-data control in [7]. Besides, the authors in [8] explored first-order leader-following consensus algorithms under a directed fixed topology with a time-varying leader. Moreover, the results in [8] were furthered extended to the case of actuator saturation and switching topology in [9].

Due to the limited communication capacity, time delays are sometimes unavoidable in multiagent systems. Olfati-saber and Murray first analyzed the consensus problem with fixed undirected topology and time delays by a frequency domain approach in [1]. Based on the reduced-order Lyapunov-Krasovskii function and linear matrix inequalities (LMIs), Lin and Jia considered the averaged consensus problem with a switching topology and time-varying delays in [10]. In addition, Hu and Lin analyzed the second-order consensus problem for multiple agents with time-varying delays in terms of the Lyapunov-Razumikhin method in [11]. Note that [1, 10, 11] studied leaderless consensus problem with time delays. For leader-follower networks, Hu and Hong [12] investigated the second-order consensus problems with fixed and switching topologies with time delays, and Tang et al. generalized the results of [12] to the case of nonuniform time delays in [13], while the scenario in the presence of detectable time delays was addressed in [14]. It is worth pointing out that the velocity of the leader in [1214] is a constant, and the time delays exist only in the transmission of position between neighbors.

In this paper, we consider the second-order leader-following consensus problem for multi-agent systems, with time delays existing in the transmission of both velocity and position. Additionally, the velocity of the leader is not restricted to be a constant but a linear function of time; that is, the acceleration of the leader can be a nonzero constant. Furthermore, we investigate the consensus problem under both fixed and switching directed interconnection topologies by means of the Lyapunov-Razumikhin function and provide the bounds of the allowable time delays.

The rest of this paper is organized as follows. Section 2 provides some preliminaries and formulates the leader-following consensus problem. With the proposed consensus protocol, Section 3 presents the convergence analysis of the case of fixed topology, and Section 4 deals with the case of switching topology. Then simulation results are presented in Section 5. Finally, the conclusion is drawn in Section 6.

The following notations will be used throughout this paper. Let be an identity matrix, , represents an zero matrix. Given a matrix, the superscripts “", and “−1" stand for matrix transposition and matrix inverse respectively; denotes the set of all eigenvalues of the matrix; denotes the spectral norm; and denote its maximum and minimum eigenvalues, respectively. denotes a diagonal matrix with diagonal elements being . Given a complex number and are its real part, imaginary part, and modulus, respectively. denotes the Kronecker product.

2. Preliminaries and Formulation

Let be a directed graph with a node set , an edge set , and a weighted adjacency matrix with nonnegative elements . Node represents agent , and an edge in is denoted by an ordered pair , where if and only if that agent can access the state information of agent . The index set of neighbors of node is denoted by . For , . Moreover, we assume that for all . The indegree and outdegree of node are, respectively, defined as , . A digraph is called balanced if for all of its nodes. The Laplacian matrix associated with is defined as and . Clearly, has at least one zero eigenvalue with a corresponding eigenvector .

A directed path is a sequence of edges in a directed graph of the form , , where . If there exists a path from node to node , we say that is reachable from . Moreover, is to said be globally reachable if there is a path from any other node to it. A directed tree is a directed graph, where there exists a node, called the root, such that any other node of the digraph can be reached by one and only one path starting at the root. A spanning tree of a digraph is a directed tree formed by graph edges that connect all the nodes of the graph.

Consider a multiagent system depicted by a graph , which consists of followers (related to graph ) and one leader (labeled as node 0) with directed edges from node 0 to some nodes. The leader adjacency matrix associated with graph is defined as a matrix , where if node 0 is a neighbor of node and otherwise.

The matrix plays an important role in the analysis of leader-following consensus problem, the following lemma shows a relationship between the positive stability of and the connectedness of graph .

Lemma 1 (see [12]). The matrix is positive stable if and only if node 0 is globally reachable in .

Remark 2. It is straightforward to verify that is positive stable if and only if the opposite graph, which is formed by changing the orientation of each arc of , has a spanning tree rooted at node 0.

All the considered followers in this paper move in a -dimensional space, with the kinematic of each follower being described by a double integrator of the form: where , , denote the position, velocity, and control input of follower , respectively. The dynamics of the leader is expressed as follows: where is the constant acceleration. The leader-following consensus of system (1)-(2) is said to be achieved if the states of followers satisfy and , .

In this paper, we are interested in discussing the leader-following consensus problem in multi-agent systems for both fixed and switching topologies, with the transmitted information time delay . To solve such a problem, the following neighbored-based protocol is used: where are control parameters, is the constant acceleration of the leader, and the time-varying delay is a continuously differentiable and bounded function. Note that in (3) depends on the position information and velocity information, all with time delay , of its neighbors and itself. When each follower can get the velocity feedback without time delay, only from the leader and itself, and when the leader moves at a constant velocity (i.e., ), protocol (3) becomes which is equivalent to protocol in [12].

To describe the variable topologies, we define a piecewise constant switching function , where is the index set associated with the elements of . The set is finite because at most a digraph with nodes is complete and has edges.

Let , ; then (3) can be written as By taking , , the closed-loop system (1) with protocol (3) can be expressed in a matrix form: where denotes the Kronecker product and are the Laplacian matrix and the leader adjacency matrix associated with , respectively.

Before discussing the convergence of system (6), we first introduce the following result on the solutions for general functional differential equations [15]. Let be a Banach space of continuous functions with a norm . Consider the following time-delay system: where , , is a continuous function and = , .

Lemma 3 (the Lyapunov-Razumikhin Theorem [15]). Let , , and be continuous, nonnegative, nondecreasing functions with , , for and . If there is a continuous function such that In addition, there exists a continuous nondecreasing function with such that the derivative of along the solution of (7) satisfies then the solution is uniformly asymptotically stable.

The function which satisfies (8) and (9) in Lemma 3 is usually called the Lyapunov-Razumikhin function.

The following lemmas are needed in the subsequent sections.

Lemma 4 (see [16]). For any two real vectors and with the same dimension, one has where is any positive definite matrix with an appropriate dimension.

Lemma 5 (the Schur Complement Theorem [17]). Let be a symmetric matrix partitioned into blocks: where both and are square; then the following properties are equivalent:(1)is positive definite;(2) and are positive definite;(3) and are positive definite.

Give a complex polynomial , where , . Substituting into , we have , where , and is the imaginary unit. The following lemma shows the relationship between the Hurwitz stability of and the related pair , .

Lemma 6 (the Hermite-Biehler Theorem [18]). The complex polynomial is Hurwitz stable if and only if the related pair , is interlaced and .

3. Consensus with Fixed Topology

This section focuses on the convergence analysis of (6) with fixed topology. In this case, the subscript can be dropped for simplicity. Denote ; we can obtain an error dynamics of (3) as follows: where

By the Newton-Leibnitz formula, we have Thus, system (12) can be rewritten as where .

The matrix plays a key role in the convergence analysis of (15); the following lemma shows a sufficient and necessary condition that can guarantee the Hurwitz stability of .

Lemma 7. The matrix is Hurwitz stable if and only if is positive stable and the control parameters satisfy .

Proof. Let be an eigenvalue of . Then, one has where is the th eigenvalue of . Therefore, the Hurwitz stability of matrix is equivalent to that of the polynomial: . Substituting into , one gets = + . Namely, = + and = . According to Lemma 6, is Hurwitz stable if and only if the following conditions hold:(i) has two distinct real roots ;(ii)the interlaced condition holds; that is, , where is the unique root of ;(iii).
Considering condition (i), has two distinct real roots if and only if = + . Noting that , since is positive stable, condition (i) holds obviously. In addition, the roots in condition (ii) can be expressed as , and . It is easy to check . Furthermore, using the fact that , the condition (iii) holds.

Theorem 8. For system (12), suppose that the control parameters satisfy Then, there exists a constant (which will be estimated in the following (22)) such that when , namely, the leader-following consensus is reached asymptotically if and only if the graph has a spanning tree.

Proof. Sufficiency. Since the graph has a spanning tree, by Remark 2, is positive stable. Then, it follows from Lemma 7 that is Hurwitz stable. Therefore, there exists a positive definite matrix such that Take the Lyapunov-Razumikhin function . Obviously, . In addition, based on (15), we have where the inequality is obtained from Lemma 4 by setting , , , , . Then, take for some constant . In the case of , , the time derivative of gives Consequently, for some constant if Hence, system (12) is asymptotically stable, invoking Lemma 3.
Necessity. As a special case of (15), the system is asymptotical stable, which implies that is Hurwitz stable. Hence, by Lemma 7, is positive stable, and then it follows from Remark 2 that the graph has a spanning tree.

Remark 9. In the special case where and for , that is, the system is free of time delay and all the followers have full access to the leader, system (3) becomes which is equivalent to the protocol in [19].

4. Consensus with Switching Topology

Consider system (6) with switching topology. Denote ; (6) can be equivalently expressed as follows: where Following the lines of the analysis of the case with fixed topology, we have Then, (24) can be written as where .

Before moving on, we first recall some conditions under which the matrix is positive definite. In [12], Hu and Hong proved that is positive definite when the graph is balanced and node 0 is globally reachable, while the authors presented the corresponding improved result in [7].

Lemma 10 (see [7]). Suppose that the weights for edges of satisfy the following assumptions.(A1), ,(A2), ,(A3) node 0 is globally reachable in ,where denotes the index set of neighbors of node 0, that is, ; then is positive definite.

Note that and are the indegree and outdegree of node in , respectively. Then, the assumption (A1) in Lemma 10 implies that the in-degree of node is no less than its out-degree for . Clearly, the condition that is balanced is a special case of assumptions (A1) and (A2). Also, changing the orientation of each arc of the graph, the assumption (A3) is equivalent to a spanning tree (rooted at node 0) contained in .

Based on Lemma 10 and the fact that the set is finite, when the weights for edges of satisfy the assumptions of Lemma 10, is well defined. Now, we give the main result of leader-following consensus problem with switching topology.

Theorem 11. For system (24), suppose that the control parameters satisfy and the graph satisfies the assumptions in Lemma 10. Take (which will be estimated in the following (34)); then one has that is, the leader-following consensus is reached asymptotically.

Proof. Take the Lyapunov-Razumikhin function , where is positive definite for .
Following a similar line to the proof of Theorem 8, we can obtain Take for some constant . In the case of , , we have where From Lemma 5, is positive definite if the control parameters satisfy (28). Furthermore, is negative definite if Therefore, the conclusion follows from Lemma 3.

5. Simulations

In this section, to illustrate our theoretical results derived in the above section, we provide two numerical simulations. For simplicity, let if agent is a neighbor of agent and otherwise.

Example 12. For the fixed topology case, we consider a multi-agent system consisting of one leader and five followers with the interaction graph given in Figure 1. It can be noted that has a spanning tree. With simple calculations, we can obtain that and . Take and ; then . Figures 2(a) and 2(b) show, respectively, the position errors and velocity errors when . We can see that the five followers can track the leader; Figures 3(a) and 3(b) show, respectively, the position errors and velocity errors when and , and in this case . From Figure 3, it can be seen that the tracking errors become unbounded.

Example 13. For the switching topology case with (i.e., the agents move in a plane), we consider a multiagent system consisting of one leader and four followers. The interaction topology switches from a set as shown in Figure 4. Clearly, the weights of both and satisfy the assumptions in Lemma 10. With simple calculations, we can obtain that . Take ; Figures 5(a) and 5(b) illustrate, respectively, the position evolutions and velocity evolutions with . we can see that the four followers can track the leader.

6. Conclusion

In this paper, we consider a second-order consensus problem for a multi-agent system with a constant acceleration leader and time-varying delays. Based on the Lyapunov-Razumikhin function and algebraic theory, we give a sufficient and necessary condition when the interaction topology is fixed and present a sufficient condition when the interaction topology is switching and the weights of graph satisfy some assumptions. Furthermore, two numerical simulations are provided to illustrate the results.

Acknowledgments

The authors would like to thank the anonymous reviewers and Editor Professor Lenci for their insightful comments and suggestions. This research is supported by NSFC (61104141, 61170309), NSFC Tianyuan foundation (11126104), and Chinese Universities Specialized Research Fund for the Doctoral Program (20110185110020).

References

  1. 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. View at: Publisher Site | Google Scholar
  2. 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. View at: Publisher Site | Google Scholar
  3. R. Olfati-Saber, “Flocking for multi-agent dynamic systems: algorithms and theory,” IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006. View at: Publisher Site | Google Scholar
  4. W. Ren and R. W. Beard, Distributed Consensus in Multi-Vehicle Cooperative Control, Springer, London, UK, 2008.
  5. W. Ren and Y. Cao, Distributed Coordination of Multi-Agent Networks, Springer, London, UK, 2011.
  6. Y. Hong, J. Hu, and L. Gao, “Tracking control for multi-agent consensus with an active leader and variable topology,” Automatica, vol. 42, no. 7, pp. 1177–1182, 2006. View at: Publisher Site | Google Scholar
  7. Z.-J. Tang, T.-Z. Huang, J.-L. Shao, and J.-P. Hu, “Leader-following consensus for multi-agent systems via sampled-data control,” IET Control Theory and Applications, vol. 5, no. 14, pp. 1658–1665, 2011. View at: Publisher Site | Google Scholar
  8. W. Ren, “Multi-vehicle consensus with a time-varying reference state,” Systems & Control Letters, vol. 56, no. 7-8, pp. 474–483, 2007. View at: Publisher Site | Google Scholar
  9. W. Ren, “Consensus tracking under directed interaction topologies: algorithms and experiments,” IEEE Transactions on Control Systems Technology, vol. 18, no. 1, pp. 230–237, 2010. View at: Publisher Site | Google Scholar
  10. P. Lin and Y. Jia, “Average consensus in networks of multi-agents with both switching topology and coupling time-delay,” Physica A, vol. 387, no. 1, pp. 303–313, 2008. View at: Publisher Site | Google Scholar
  11. J. Hu and Y. S. Lin, “Consensus control for multi-agent systems with double-integrator dynamics and time delays,” IET Control Theory and Applications, vol. 4, no. 1, pp. 109–118, 2010. View at: Publisher Site | Google Scholar
  12. 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. View at: Publisher Site | Google Scholar
  13. Z. Tang, T. Huang, J. Hu, and J. Shao, “Leader-fowing consensus in networks of agents with nonuniform time-varying delays,” Mathematical Problems in Engineering, vol. 2012, Article ID 848942, 12 pages, 2012. View at: Publisher Site | Google Scholar
  14. W. Zhu and D. Cheng, “Leader-following consensus of second-order agents with multiple time-varying delays,” Automatica, vol. 46, no. 12, pp. 1994–1999, 2010. View at: Publisher Site | Google Scholar
  15. J. K. Hale and S. M. Verduyn Lunel, Introduction to Functional-Differential Equations, vol. 99, Springer, New York, NY, USA, 1993.
  16. M. Wu, Y. He, and J. She, Stability Analysis and Robust Control of Time-Delay Systems, Science Press, Beijing, China; Springer, Berlin, Germany, 2010.
  17. R. A. Horn and C. R. Johnson, Topics in Matrix Analysis, Cambridge University Press, Cambridge, NY, USA, 1994.
  18. K. Ogata, Discrete-Time Control Systems, Prentice Hall, Englewood Cliffs, NJ, USA, 2nd edition, 1995.
  19. W. Ren, “Second-order consensus algorithm with extensions to switching topologies and reference models,” in Proceedings of the American Control Conference (ACC '07), pp. 1431–1436, July 2007. View at: Publisher Site | Google Scholar

Copyright © 2013 Hong Xia 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.

926 Views | 638 Downloads | 4 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder