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Mathematical Problems in Engineering
Volume 2018, Article ID 6059574, 12 pages
https://doi.org/10.1155/2018/6059574
Research Article

Consensus of Fractional-Order Multiagent Systems with Double Integral and Time Delay

1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
2College of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China

Correspondence should be addressed to Jun Liu; nc.ude.tiuc@nujuil

Received 1 January 2018; Accepted 4 April 2018; Published 20 May 2018

Academic Editor: Luis Gracia

Copyright © 2018 Jun 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 is devoted to the consensus problems for a fractional-order multiagent system (FOMAS) with double integral and time delay, the dynamics of which are double-integrator fractional-order model, where there are two state variables in each agent. The consensus problems are investigated for two types of the double-integrator FOMAS with time delay: the double-integrator FOMAS with time delay whose network topology is undirected topology and the double-integrator FOMAS with time delay whose network topology is directed topology with a spanning tree in this paper. Based on graph theory, Laplace transform, and frequency-domain theory of the fractional-order operator, two maximum tolerable delays are obtained to ensure that the two types of the double-integrator FOMAS with time delay can asymptotically reach consensus. Furthermore, it is proven that the results are also suitable for integer-order dynamical model. Finally, the relationship between the speed of convergence and time delay is revealed, and simulation results are presented as a proof of concept.

1. Introduction

In the past decade, an increasing number of scholars have been interested in the consensus problems for multiagent systems with potential applications in biology, control engineering, and physics. Typical applications include flocking [1, 2], swarming [3, 4], formation control [5, 6], sensor networks [79], and many other areas. For multiagent systems, consensus means that a group of agents reach an agreement on a common value by exchanging local information with their neighbours. However, due to the limitation of the communication device and the undesirable communication environment, time delays are ubiquitous in the information exchange. The existence of time delays may have a bad impact on the consensus for multiagent systems, so it is meaningful to study the property of multiagent systems with time delays. Up till now, consensus problems involving multiagent systems with time delays have been resolved by many scholars. In [10], the authors discussed the average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. In [11], the authors investigated consensus problems of a class of second-order continuous-time multiagent systems with time delay and jointly connected topologies. In [12], the authors investigated the leader-following stationary consensus problem for second-order multiagent systems with time-varying communication delay and switching topology. In [13], the authors proposed a distributed protocol for consensus of second-order multiagent systems with inherent nonlinear dynamics and communication time delay. In [14], the authors solved control problems of agents achieving consensus motions in presence of nonuniform time delays. In [15], the authors investigated robust consensus control problems involving input delays for uncertain multiagent systems.

Note that all above multiagent systems concern integer-order dynamics. In fact, many natural phenomena in the complex environment cannot be accurately explained by using the framework of integer-order dynamics, such as electromagnetic waves, viscoelasticity, and the heat flux of the thermal field of the furnace wall [16], while more dynamic properties of natural phenomena can be better revealed by using the framework of fractional-order dynamics, such as food seeking of microbes, collection motion of bacteria in lubrications, and underwater vehicles operating in lentic lakes [17]. Fractional-order derivatives provide an excellent instrument for the description of memories and hereditary effects of various materials and processes which are neglected in classical integer-order dynamics. In addition, it has been stated in [18] that the well-studied integer-order systems were just the special cases of fractional-order ones. Consensus of multiagent systems based on the fractional-order models was early studied by Cao et al. [17]. After that, the research results have been continuously springing up about consensus problems of fractional-order multiagent systems with time delays. In [19], a necessary and sufficient condition was derived to ensure the consensus of fractional-order systems with identical input delays over a directed topology. In [20, 21], the authors successively studied the fractional-order system consensus control with communication delays, where homogeneous dynamics and heterogeneous dynamics were investigated. In [22], the consensus of linear and nonlinear fractional-order multiagent systems with input time delay was studied. In [23], consensus problems were investigated for fractional-order multiagent systems with nonuniform time delays. Recently, a new fractional-order multiagent system with double-integrator model was proposed by authors in [2426]. Based on the fractional-order stability theory, Mittag-Leffler function, and Laplace transform, the consensus problem of fractional-order multiagent systems with double integral under fixed topology was studied in [24]. By applying Mittag-Leffler function, Laplace transform, and dwell time technique, the consensus of fractional-order multiagent systems with double integral under switching topology was investigated in [25]. The consensus for the fractional-order double-integrator multiagent systems based on the sliding mode estimator was studied in [26]. So far, to the best of our knowledge, there are very few research works done on the consensus problems of fractional-order multiagent systems with double integral and time delay.

Motivated by above analysis, the consensus problems are investigated for the two types of fractional-order multiagent system (FOMAS) with double integral and time delay: the FOMAS with double integral and time delay whose network topology is undirected topology and the FOMAS with double integral and time delay whose network topology is directed topology with a spanning tree in this paper. Firstly, a distributed control protocol based on state feedback of neighbours is designed. By applying the graph theory tools, the closed-loop double-integrator fractional-order dynamics with time delay are established and the consensus problems of the double-integrator FOMAS with time delay are transformed into the problems of the system matrix eigenvalues of the double-integrator FOMAS. By employing Laplace transform of Caputo derivative, the characteristic equation of the system matrix of the double-integrator FOMAS is derived. Then, based on the frequency-domain analysis and the matrix theory tool, the two maximum tolerable delays are obtained to ensure consensus for the double-integrator FOMAS with time delay. The main contribution of this article lies in the research on the double-integrator fractional-order dynamics which can better reveal the essential characteristic or behavior of an object in the complex environment and the consensus of the double-integrator FOMAS with time delay whose network topology is, respectively, undirected topology and directed topology.

The remainder of the paper is organized as follows. In Section 2, some basic preliminaries about algebraic graph theory and fractional calculus are shown out. In Sections 3 and 4, problem statement, main results, and some corollaries for the two types of double-integrator FOMAS with time delay whose network topology is, respectively, undirected topology and directed topology are proposed. In Section 5, several simulation results are simulated to illustrate the correctness of the proposed theoretical results. Finally, concluding remarks are drawn in Section 6.

2. Preliminaries

In this section, basic preliminary knowledge about algebraic graph theory and fractional calculus is introduced for the following analysis.

2.1. Algebraic Graph Theory

Algebraic graph theory is a practical framework for analyzing consensus problems. Let be an interaction graph of order , where is the set of nodes, is the set of edges, and is a weighted adjacency matrix. The node’s indices belong to a finite index set . If there is a directed edge which is from node to node , then ; otherwise, . Suppose that there are no self-loops; that is, . If for any , is an undirected graph; if there exists , is a directed graph. The set of neighbours of node is denoted by . The Laplacian matrix of the graph is defined as , where is a diagonal matrix with . A directed path is a sequence of ordered edges of the form , and if there is a path from every node to every other node, the graph is said to be strongly connected. Moreover, a directed graph is said to have a spanning tree, if there exists a node such that every other node has a directed path to this node. It is easy to verify that has at least one zero eigenvalue with a corresponding eigenvector , where .

Lemma 1 (see [27]). For an undirected graph, the Laplacian matrix has a simple zero eigenvalue with an associated eigenvector and other eigenvalues are positive real numbers; that is, , if and only if is connected.

Lemma 2 (see [27]). For a directed graph, the Laplacian matrix has a simple zero eigenvalue with an associated eigenvector and other eigenvalues have a positive real part; that is, , if and only if has a directed spanning tree.

2.2. Fractional Calculus

There are many different definitions of fractional-order operators; the Caputo fractional operator and Riemann-Liouville (R-L) fractional operator are the most broadly used to analyze fractional-order dynamical systems. The major advantage of Caputo fractional derivative over R-L fractional derivative is the initial conditions for fractional differential equations can take on the same form as the traditional integer-order differential equations. Therefore, this paper adopts the Caputo fractional operator to study the system dynamics. For an arbitrary real number , the Caputo derivative is defined as where denotes the order of the derivative and is the Gamma function:

Let represent the Laplace transform of the function ; that is, , and let replace ; then the Laplace transform is of the Caputo derivative as follows: where and .

3. Problem Statement

Consider a double-integrator FOMAS being made up of agents. Each agent is regarded as a node in the graph . Each edge corresponds to an available information channel between agents and . The dynamical equations of agent of the double-integrator FOMAS are described as follows: where and , respectively, represent the th agent’s two states, and , respectively, denote the -order Caputo derivatives of and   (), and represents control input.

Definition 3. The double-integrator FOMAS (4) reaches consensus, if and only if the states of agents satisfy

Assume the following control protocol is given by where , are the scale coefficients, is the th entry of the adjacency matrix in , and denotes the neighbour set of th agent.

Define the state vector of a single agent as ; the joint state vector of the double-integrator FOMAS (4) is .

Define two matrixes:

Let ; the double-integrator FOMAS (4) without time delay can be described as: where denotes the -order Caputo derivative of .

Consider that time delay cannot be avoided; the control protocol involving time delay can be given by where is the time delay.

Now, we adopt protocol (9); the double-integrator FOMAS (4) with time delay can be described as

4. Main Results

4.1. Consensus of the Double-Integrator FOMAS with Time Delay over Undirected Topology

Theorem 4. Suppose that a double-integrator FOMAS is composed of agents whose network topology is connected and undirected. By the distributed control protocol (9), the double-integrator FOMAS (10) with time delay can asymptotically reach consensus, if the value of is given by the following equation: where is the maximum eigenvalue of the Laplacian matrix .

Proof. Applying the frequency-domain method to analyze the double-integrator FOMAS (10) and defining as the Laplace transform of , it is obvious that and For the stability analysis of a fractional-order system, Matignon has pointed out that the stability of the linear fractional-order system can be judged by characteristic roots of the system function and all the poles of the system function have negative real parts if the linear fractional-order system is stable [28, 29]. So we can analyze the root’s position of the characteristic polynomial to study consensus of the double-integrator FOMAS (10). Specifically, as increases continuously from zero, the characteristic root will change continuously from the left half plane (LHP) to the right half plane (RHP). Once the characteristic root passes through the imaginary axis to the RHP, the double-integrator FOMAS (10) will be unstable and cannot achieve consensus. Therefore, it is necessary for us to consider a critical condition where the nonzero characteristic root is located just on the imaginary axis and the corresponding time delay is just the maximum tolerable delay of the double-integrator FOMAS (10).
Suppose that is a characteristic root of , is the corresponding eigenvector, and ; then we have the following equation: Note that all the complex roots of each appear in conjugated pairs; it is just for us to study the situation where . Since the elements of the vector obtained by calculating the left part of (14) are equal to zero, it can be easy to obtain that . Then, on the left side of (14), multiply by (the conjugate transpose of ); the following equation can be obtained: where Because of , we get because of , we get Finally, we can get Define there is Because , it is easy to know that , and .
So (21) can be simplified to the following equation:Take modulus of the both sides of (22), and, according to Lemma 1, we can get the following inequality: it is obvious that is an increasing function for , and if , we can get ; that is, inequality (23) is true.
In addition, the principal value of the argument of is as follows: Let Define . Obviously, . Then the first derivative of is as follows: whereConstruct the following function: then the first derivative of is as follows: Note that the denominator of is greater than ; we just need to consider the molecule of . denotes the molecule of ; we have Due to and , one has for . Moreover, ; thus, , which means that . Therefore, is a decreasing function for ; when , one has Note that the above inequality (31) is based on the assumption that ’s root of the double-integrator FOMAS (10) exists on the imaginary axis. If we let , we can obtain the following inequality: Inequality (32) contradicts inequality (31). That is, as long as , we can ensure that all the characteristic roots of are located in the LHP of the imaginary axis; the double-integrator FOMAS (10) can achieve consensus. On the other hand, when , is a characteristic root of , and the corresponding vector makes hold. So is called the maximum tolerable delay. In addition, when , the double-integrator FOMAS (10) must have characteristic roots of which are located in the RHP of the imaginary axis. According to the principle of stability, the double-integrator FOMAS (10) cannot achieve consensus.
This completes the proof of Theorem 4.

Corollary 5. Suppose that a double-integrator FOMAS is composed of agents whose network topology is connected and undirected. When the fractional order satisfies , by the distributed control protocol (9), the double-integrator FOMAS (10) with time delay can asymptotically reach consensus, if the value of is given by the following equation: where is the maximum eigenvalue of the Laplacian matrix .

4.2. Consensus of the Double-Integrator FOMAS with Time Delay over Directed Topology

Theorem 6. Suppose that a double-integrator FOMAS is composed of agents whose network topology is directed and has a spanning tree. By the distributed control protocol (9), the double-integrator FOMAS (10) with time delay can asymptotically reach consensus, if where and is the th eigenvalue of the Laplacian matrix . The value of is given by the following equation:

Proof. Similarly to the proof method of Theorem 4, we suppose that is the characteristic root of the system matrix on the imaginary axis, is the corresponding eigenvector, and and . According to Lemma 2, we can get the following equation: Taking modulus of the both sides of (37), we get where is an increasing function for ; thus is also an increasing function for .
Calculate the principal value of the argument of (37); we get According to the definition of in (37), we get so one has Due to , the possible values of must be nonzero eigenvalues of the Laplacian matrix of the graph ; that is, , which makes the maximum tolerable delay minimized. So when . If we let , one has Inequality (42) contradicts inequality (41). That is, when , the eigenvalues of the system matrix of the double-integrator FOMAS (10) cannot reach or cross the imaginary axis; then the double-integrator FOMAS (10) will remain stable and consensus of the double-integrator FOMAS (10) can be achieved. On the other hand, when , there must exist at least one eigenvalue of the system matrix of the double-integrator FOMAS (10) in the RHP; then the states of the double-integrator FOMAS (10) are no longer convergent; the double-integrator FOMAS (10) cannot reach consensus.
This completes the proof of Theorem 6.

Corollary 7. Suppose that a double-integrator FOMAS is composed of agents whose network topology is directed and has a spanning tree. When the fractional order satisfies , by the distributed control protocol (9), the double-integrator FOMAS (10) with time delay can asymptotically reach consensus, if [30] and where , and is the th eigenvalue of the Laplacian matrix . The value of is given by the following equation:

5. Simulation Results

To illustrate the correctness of the theoretical results, numerical simulations will be given in this section.

Example 1 (simulations for Theorem 4). Consider a double-integrator FOMAS with four agents whose dynamics are described by (10) with , and . The connected interaction graph of the double-integrator FOMAS (10) is undirected and is shown in Figure 1, and the Laplacian matrix of which is as follows: It is easy to obtain that the four eigenvalues of the Laplacian matrix are 0, 2, 4, and 4, respectively; then . According to Theorem 4, the maximum tolerable delay of the double-integrator FOMAS (10) is . Assume that the initial states of the double-integrator FOMAS (10) are taken as , , , and . Then three different time delays are used for simulation: (1) , (2) , and (3) .
Figures 2, 3, and 4 show the trajectories of all the agents’ states . Figures 5, 6, and 7 show the trajectories of all the agents’ states . It is clear that the double-integrator FOMAS (10) can reach consensus.
By comparing the simulation results of Figures 2, 3, and 4 and Figures 5, 6, and 7, we can find that as the time delay stays away from the maximum tolerable delay when the communication topology and the fractional-order of the double-integrator FOMAS (10) stay the same, the convergence speed will become faster, whereas the convergence speed will become slower as the time delay is close to the maximum tolerable delay .
In order to make a comparison, under the same conditions, suppose that . Figures 8 and 9 show the trajectories of all the agents’ states and , respectively. It is obvious that the double-integrator FOMAS (10) cannot reach consensus.
The above simulation results are consistent with Theorem 4. So the correctness of Theorem 4 is validated.

Figure 1: The connected interaction topology in Example 1.
Figure 2: The trajectories of when (1) in Example 1.
Figure 3: The trajectories of when (2) in Example 1.
Figure 4: The trajectories of when (3) in Example 1.
Figure 5: The trajectories of when (1) in Example 1.
Figure 6: The trajectories of when (2) in Example 1.
Figure 7: The trajectories of when (3) in Example 1.
Figure 8: The trajectories of when in Example 1.
Figure 9: The trajectories of when in Example 1.

Example 2 (simulations for Theorem 6). Consider a double-integrator FOMAS with four agents whose dynamics are described by (10) with , and . The directed graph of the double-integrator FOMAS (10) has a spanning tree and is shown in Figure 10, the Laplacian matrix of which is as follows: It is easy to obtain that the four eigenvalues of the Laplacian matrix are and , respectively. According to Theorem 6, the maximum tolerable delay of the double-integrator FOMAS (10) is . Assume that the initial states of the double-integrator FOMAS (10) are taken as , , , and . Then three different time delays are used simulation: (1) , (2) , and (3) .
Figures 11, 12, and 13 show the trajectories of all the agents’ states . Figures 14, 15, and 16 show the trajectories of all the agents’ states . It is clear that the double-integrator FOMAS (10) can reach consensus.
By comparing the simulation results of Figures 11, 12, and 13 and Figures 14, 15, and 16, we can find that as the time delay stays away from the maximum tolerable delay when the communication topology and the fractional-order of the double-integrator FOMAS (10) stay the same, the convergence speed will become faster, whereas the convergence speed will become slower as the time delay is close to the maximum tolerable delay .
In order to make a comparison, under the same conditions, suppose that . Figures 17 and 18 show the trajectories of all the agents’ states and , respectively. It is obvious that the double-integrator FOMAS (10) cannot reach consensus.
The above simulation results are consistent with Theorem 6. So the correctness of Theorem 6 is validated.

Figure 10: The connected interaction topology in Example 2.
Figure 11: The trajectories of when (1) in Example 2.
Figure 12: The trajectories of when (2) in Example 2.
Figure 13: The trajectories of when (3) in Example 2.
Figure 14: The trajectories of when (1) in Example 2.
Figure 15: The trajectories of when (2) in Example 2.
Figure 16: The trajectories of when (3) in Example 2.
Figure 17: The trajectories of when in Example 2.
Figure 18: The trajectories of when in Example 2.

6. Conclusion

In this paper, the consensus control problems of the double-integrator FOMAS with time delay are studied. First of all, the consensus problem is investigated for the double-integrator FOMAS with time delay over undirected topology, and a maximum tolerable delay is obtained to ensure the consensus. Then, the consensus problem is investigated for the double-integrator FOMAS with time delay over directed topology, and a maximum tolerable delay is obtained to ensure the consensus. Moreover, extending above results, some corollaries are obtained for the corresponding integer-order multiagent systems, which are the same as traditional integer-order systems. Finally, the relationship between the speed of convergence and time delay is revealed, and the correctness of our theoretical results is validated by the simulations.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the Science and Technology Department Project of Sichuan Province of China (Grants nos. 2014FZ0041, 2017GZ0305, and 2016GZ0220), the Education Department Key Project of Sichuan Province of China (Grant no. 17ZA0077), and the National Natural Science Foundation of China (Grant no. U1733103).

References

  1. R. Olfati-Saber, “Flocking for multi-agent dynamic systems: algorithms and theory,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. H. Su, X. Wang, and Z. Lin, “Flocking of multi-agents with a virtual leader,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 54, no. 2, pp. 293–307, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. V. Gazi and K. M. Passino, “Stability analysis of swarms,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 48, no. 4, pp. 692–697, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. Y. Liu, K. M. Passino, and M. Polycarpou, “Stability analysis of M-dimensional asynchronous swarms with a fixed communication topology,” Proceedings of the American Control Conference, vol. 2, pp. 1278–1283, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. J. A. Fax and R. M. Murray, “Information flow and cooperative control of vehicle formations,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1465–1476, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. F. Xiao, L. Wang, J. Chen, and Y. Gao, “Finite-time formation control for multi-agent systems,” Automatica, vol. 45, no. 11, pp. 2605–2611, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. R. Olfati-Saber and J. S. Shamma, “Consensus filters for sensor networks and distributed sensor fusion,” in Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05, pp. 6698–6703, esp, December 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Shen, Z. Wang, and Y. S. Hung, “Distributed H(infinity)-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case,” Automatica, vol. 46, no. 10, pp. 1682–1688, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  9. B. Shen, Z. Wang, and X. Liu, “A stochastic sampled-data approach to distributed H∞ filtering in sensor networks,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 9, pp. 2237–2246, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  10. 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 MathSciNet · View at Scopus
  11. P. Lin and Y. Jia, “Consensus of a class of second-order multi-agent systems with time-delay and jointly-connected topologies,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 55, no. 3, pp. 778–784, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. C. Liu and F. Liu, “Consensus problem of second-order multi-agent systems with time-varying communication delay and switching topology,” Journal of Systems Engineering and Electronics, vol. 22, no. 4, pp. 672–678, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Chen, J. Wang, Y. Zhang, and Z. Liu, “Second-order consensus of nonlinear multi-agent systems with restricted switching topology and time delay,” Nonlinear Dynamics, vol. 78, no. 2, pp. 881–887, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. M. Shi and K. Qin, “Distributed control for multiagent consensus motions with nonuniform time delays,” Mathematical Problems in Engineering, 10 pages, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  15. P. Li and K. Qin, “Distributed robust H-infinity consensus control for uncertain multiagent systems with state and input delays,” Mathematical Problems in Engineering, 15 pages, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  16. I. Podlubny, Fractional Differential Equations, vol. 198 of Mathematics in Science and Engineering, Academic Press, San Diego, Calif, USA, 1999. View at MathSciNet
  17. Y. C. Cao, Y. Li, W. Ren, and Y. Q. Chen, “Distributed coordination of networked fractional-order systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 2, pp. 362–370, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Tricaud and Y. Chen, “Time-optimal control of systems with fractional dynamics,” International Journal of Differential Equations, vol. 2010, Article ID 461048, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Shen and J. Cao, “Necessary and sufficient conditions for consensus of delayed fractional-order systems,” Asian Journal of Control, vol. 14, no. 6, pp. 1690–1697, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. H. Y. Yang, X. L. Zhu, and K. C. Cao, “Distributed coordination of fractional order multi-agent systems with communication delays,” Fractional Calculus and Applied Analysis, vol. 17, no. 1, pp. 23–37, 2013. View at Google Scholar
  21. H.-Y. Yang, L. Guo, X.-L. Zhu, K.-C. Cao, and H.-L. Zou, “Consensus of compound-order multi-agent systems with communication delays,” Central European Journal of Physics, vol. 11, no. 6, pp. 806–812, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Zhu, B. Chen, and J. Yang, “Consensus of fractional-order multi-agent systems with input time delay,” Fractional Calculus and Applied Analysis, vol. 20, no. 1, pp. 52–70, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. J. Liu, K. Qin, W. Chen, P. Li, and M. Shi, “Consensus of fractional-order multiagent systems with nonuniform time delays,” Mathematical Problems in Engineering, Article ID 2850757, 9 pages, 2018. View at Publisher · View at Google Scholar · View at MathSciNet
  24. C. Yang, W. Li, and W. Zhu, “Consensus analysis of fractional-order multiagent systems with double-integrator,” Discrete Dynamics in Nature and Society, 8 pages, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  25. S. Shen, W. Li, and W. Zhu, “Consensus of fractional-order multiagent systems with double integrator under switching topologies,” Discrete Dynamics in Nature and Society, 7 pages, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  26. J. Bai, G. Wen, A. Rahmani, and Y. Yu, “Consensus for the fractional-order double-integrator multi-agent systems based on the sliding mode estimator,” IET Control Theory & Applications, vol. 12, no. 5, pp. 621–628, 2018. View at Publisher · View at Google Scholar
  27. C. Godsil and G. Royle, Algebraic Graph Theory, Springer, New York, NY, USA, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  28. D. Matignon, “Stability results for fractional differential equations with applications to control processing,” Computational Engineering in Systems Applications, vol. 2, pp. 963–968, 1996. View at Google Scholar
  29. D. Matignon, “Stability properties for generalized fractional differential systems,” Proceedings of the Fractional Differential Systems: Models, Methods and Applications, vol. 5, pp. 145–158, 1998. View at Publisher · View at Google Scholar
  30. M. Shi, K. Qin, and P. Li, “Consensus of multiple mobile robot systems with directed topologies and nonuniform time delays,” in Proceedings of the International Conference on Mechatronics and Control, ICMC 2014, pp. 787–790, China, July 2014. View at Publisher · View at Google Scholar · View at Scopus