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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 601652, 4 pages
http://dx.doi.org/10.1155/2013/601652
Research Article

Multiagent Consensus Control under Network-Induced Constraints

1State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
2Kimchaek Industry University, Pyongyang 999093, Republic of Korea

Received 29 March 2013; Accepted 19 August 2013

Academic Editor: Anyi Chen

Copyright © 2013 Won Il Kim 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

Mean consensus problem is studied using a class of discrete time multiagent systems in which information exchange is subjected to some network-induced constraints. These constraints include package dropout, time delay, and package disorder. Using Markov jump system method, the necessary and sufficient condition of mean square consensus is obtained and a design procedure is presented such that multiagent systems reach mean square consensus.

1. Introduction

Cooperative control of networked multiagent systems by information exchange has received extensive attention presently, because of their extensive applications in flocking, swarming, distributed sensor fusion, attitude alignment, and so forth (see [1, 2] for surveys). An important problem for cooperative control is to design an appropriate control law such that a multiagent system reaches consensus in the presence of insecure information exchange. Distributed cooperative control of networked multiagent systems has been investigated in various perspectives [37]. In [3], the leaderless consensus problem is studied. The problem of consensus with leader node was researched in [47]. For networked multiagent systems of linear dynamics, consensus using state feedback or output feedback was analysed in [8, 9].

Unmodelled time delay during the design phase is an important factor that may affect the performance of dynamical systems. It can even, in some situation, cause instability of a system. In these years, consensus in networked multiagent systems with time delay was discussed using linear matrix inequality (LMI) method [1012]. In [10], the average-consensus problem for continuous-time multiagents with switching topology and time delay was studied. The work of [11] investigated the average consensus problem in undirected networks with fixed and switching topologies under time-varying communication delays. The consensus problem was solved in [12] on directed graphs of the multiagent system with model uncertainty and time delay.

In the information exchange of network, there are not only time-delay but also other network-induced constraints. The other network-induced constraints, which include package dropout and package disorder, also affect the consensus of networked multiagent systems. However, not many works have studied multiagent systems with these network-induced constraints. Based on Markov jump system method [1315], this paper considers mean square consensus of multiagent systems of first-order integrator under network-induced constraints such as package dropout, time delay, and package disorder. By system transformation, the necessary and sufficient condition of mean square consensus problem is provided and a corresponding design algorithm is given.

The remainder of the paper is organized as follows. Section 2 contains the formulation of the problem and terminology. The main results are presented in Section 3. Section 4 provides the numerical simulation and Section 5 draws conclusions.

2. Problem Formulation and Preliminaries

In this paper, is used to denote the set of all nonnegative integers. The identity matrix is denoted by . The th row of is denoted by . If a matrix is positive (negative) definite, it is denoted by . The notation within a matrix represents the symmetric term of the matrix. The expected value is represented by .

Here a discrete-time system is considered that it consists of 2 agents. Each agent is a first-order integrator, which, where and are the state at time step , and are the control at time step , and is constant. The two agents exchange their state through two communication channels: channel no. 1 and channel no. 2. At each , agent 1 transmits to agent 2 through channel no. 1. Agent 2 utilizes as the information obtained from channel no. 1 at . Due to random package dropout, time delay, and package disorder in communication, the receiving scenarios in the side of agent 2 at are various. Agent 2 may receive one package from channel no. 1 at . The package is sent by agent 1 at no later than . After received, is examined to see whether it is of disorder (i.e., whether agent 2 has received any packages sent later than ). If is not of disorder, . If is of disorder, is discarded and . Agent 2 may receive severe package , from channel no. 1 at . Except the newest with , these packages are discarded. If is not of disorder, . If   is of disorder, it is also discarded and . Agent 2 may receive no package from channel no. 1 at . In this case, .

From the above mechanism, it is seen that with some random constrained by On , we adopt an assumption which is made by some researchers in networked control [16]; that is, is assumed to be a Markov chain taking values in a finite set with transition probabilities: where is a given nonnegative integer. The transition probability matrix is also known. The expression (4) of displays that, for the reason of constraint (2), when . Thus, we have described the communication in channel no. 1 using Markov chain . The same method is applied to describe the communication in channel no. 2. Agent 1 obtains from channel no. 2 at , where is a Markov chain taking values in a known set with a known transition probability matrix The goal of agents 1 and 2 is a prescribed state . In this paper, agent 1 is aware of while agent 2 is not. Consequently, agent 1 employs control law but agent 2 employs control law where is the control parameter.

The above multiagent system is said to be mean square consensus if , , , , Our objective is to design such that the two agents reach mean square consensus.

3. Main Result

Define Then from (1), (6), (7), and (9), we have Further, denote Obviously, mean square consensus (8) is equivalent to . Using (11), system (10) is transformed into where On system (12), [16] presented the following.

Lemma 1. Suppose that Markov chains and are independent. System (12) achieves if and only if there exist positive definite matrices , , such that

Actually, the condition in Lemma 1 can be converted using Schur complement.

Lemma 2 (see [17]). Let be given partitioned matrix. Then if and only if and .

Through the above converting, the necessary and sufficient condition is obtained from mean square consensus of the multiagent system.

Theorem 3. Suppose that Markov chains and are independent. The multiagent system in Section 2 achieves mean square consensus if and only if there exist and positive definite matrices , , , such that

In order to deal with the condition in Theorem 3 using Cone Complementarity Linearisation algorithm [18], for , we construct LMI which is denoted by . The following is our design steps:

Step 1. Specify an enough small real number and an enough large integer . Set . Find feasible , , and , and satisfy . If there is none, exit.

Step 2. Solve the LMI problem and obtain , and .

Step 3. If , let and terminate. Otherwise, and go to Step 4.

Step 4. If , exist. Otherwise, go to Step 2

It should be pointed out that the above method is easy to be extended to agents when . Among agents, there are communication channels. We utilize independent Markov chains to describe communication in these channels and can arrive at a similar result as Theorem 3.

4. Numerical Example

In the numerical example, we give , , , and transition probability of and is given as Using the design steps in Section 3, we get . Figure 1 shows the state response of two agents with It can be seen that and converge at .

601652.fig.001
Figure 1: State response of two agents.

5. Conclusion

The consensus control problem of multiagent systems of first-order integrator is studied under network-induced constraints. A new model is presented to describe the network communication with package dropout, time delay, and package disorder. For the new model, the definition of mean square consensus is given multiagent systems. Further, the necessary and sufficient condition of mean square consensus is proposed in the form of matrix inequalities. Based on this condition and Cone Complementarity Linearisation algorithm, a consensus control law can be designed to make systems reach mean square consensus.

References

  1. R. O. Saber, J. Fax, and R. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007. View at Publisher · View at Google Scholar
  2. W. Ren, R. Beard, and E. Atkins, “A survey of consensus problems in multi-agent coordination,” in Proceedings of the American Control Conference (ACC '05), pp. 1859–1864, Portland, Ore, USA, June 2005. View at Publisher · View at Google Scholar
  3. W. Ren, R. Beard, and E. Atkins, “Information consensus in multivehicle cooperative control,” IEEE Control Systems Magazine, vol. 27, no. 2, pp. 71–82, 2007. View at Publisher · View at Google Scholar
  4. 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 · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. X. Li, X. Wang, and G. Chen, “Pinning a complex dynamical network to its equilibrium,” IEEE Transactions on Circuits and Systems, vol. 51, no. 10, pp. 2074–2087, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  6. X. F. Wang and G. Chen, “Pinning control of scale-free dynamical networks,” Physica A, vol. 310, no. 3-4, pp. 521–531, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  7. W. Ren, K. Moore, and Y. Chen, “High-order and model reference consensus algorithms in cooperative control of multivehicle systems,” Journal of Dynamic Systems, Measurement, and Control, vol. 129, no. 5, pp. 678–688, 2007.
  8. H. Zhang, F. L. Lewis, and A. Das, “Optimal design for synchronization of cooperative systems: state feedback, observer and output feedback,” IEEE Transactions on Automatic Control, vol. 56, no. 8, pp. 1948–1952, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  9. C.-Q. Ma and J.-F. Zhang, “Necessary and sufficient conditions for consensusability of linear multi-agent systems,” IEEE Transactions on Automatic Control, vol. 55, no. 5, pp. 1263–1268, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  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 · View at Google Scholar
  11. 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. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. P. Lin, Y. Jia, and L. Li, “Distributed robust H consensus control in directed networks of agents with time-delay,” Systems & Control Letters, vol. 57, no. 8, pp. 643–653, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. A. H. Tahoun and H.-J. Fang, “Adaptive stabilisation of networked control systems tolerant to unknown actuator failures,” International Journal of Systems Science, vol. 42, no. 7, pp. 1155–1164, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. J. Xiong and J. Lam, “Stabilization of linear systems over networks with bounded packet loss,” Automatica, vol. 43, no. 1, pp. 80–87, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. J. Xiong and J. Lam, “Robust H2 control of Markovian jump systems with uncertain switching probabilities,” International Journal of Systems Science, vol. 40, no. 3, pp. 255–265, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  16. Y. Xia, G. P. Liu, M. Fu, and D. Rees, “Predictive control of networked systems with random delay and data dropout,” IET Control Theory Application, vol. 3, pp. 1476–1486, 2009.
  17. S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix inequalities in System and Control Theory, vol. 15 of SIAM Studies in Applied Mathematics, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pa, USA, 1994. View at Publisher · View at Google Scholar · View at MathSciNet
  18. L. El Ghaoui and L. Oustry, “A cone complementarity linearization algorithm for static output-feedback and related problems,” IEEE Transactions on Automatic Control, vol. 42, no. 8, pp. 1171–1176, 1997.