Skip to main content

Event-Driven Communication and Control in Multi-Agent Networks

  • Chapter
  • First Online:
Introduction to Hybrid Intelligent Networks

Abstract

Event-triggered/driven control is a measurement-based (e.g., system state or output) sampling control whereas the time instants for sampling and control actions should be determined by a predefined triggering condition (i.e., a measurement-based condition). It thus can be viewed as a type of hybrid control. In a network environment, an important issue in the implementation of distributed algorithms is the communication and control actuation rules. An event-driven scheme would be more favorable in the communication and control actuation for MANs, especially for embedded, interconnected devices with limited resources.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” Syst. Control Lett., vol. 53, no. 1, pp. 65–78, 2004.

    Article  MathSciNet  Google Scholar 

  2. R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Tran. Autom. Control, vol. 51, no. 3, pp. 401–420, 2006.

    Article  MathSciNet  Google Scholar 

  3. R. Hegselmann and U. Krause, “Opinion dynamics and bounded confidence models, analysis and simulation,” J. Artif. Soc. Soc. Simul., vol. 5, no. 3, pp. 1–24, 2002.

    Google Scholar 

  4. M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Phys. Rev. E, vol. 69, 026113, 2004.

    Article  Google Scholar 

  5. G. S. Han, D. X. He, Z.-H. Guan, B. Hu, T. Li, and R.-Q. Liao, “Multi-consensus of multi-agent systems with various intelligence degrees using switched impulsive protocols,” Inform. Sci., vol. 349–350, pp. 188–198, 2016.

    Article  Google Scholar 

  6. J. Y. Yu and L. Wang, “Group consensus of multi-agent systems with directed information exchange,” Int. J. Syst. Science, vol. 43, no. 2, pp. 334–348, 2012.

    Article  MathSciNet  Google Scholar 

  7. M. Y. Zhong and C. G. Cassandras, “Asynchronous distributed optimization with event-driven communication,” IEEE Trans. Autom. Control, vol. 55, no. 12, pp. 2735–2750, 2010.

    Article  MathSciNet  Google Scholar 

  8. J. F. Wu, Q. S. Jia, K. H. Johansson, and L. Shi, “Event-based sensor data scheduling: Trade-off between communication rate and estimation quality,” IEEE Trans. Autom. Control, vol. 58, no. 4, pp. 1041–1046, 2013.

    Article  MathSciNet  Google Scholar 

  9. X. H. Ge and Q. L. Han, “Distributed event-triggered H filtering over sensor networks with communication delays,” Inform. Sci., vol. 291, pp. 128–142, 2015.

    Article  MathSciNet  Google Scholar 

  10. S. X. Wen, G. Guo, and W. S. Wong, “Hybrid event-time-triggered networked control systems: Scheduling-event-control co-design,” Inform. Sci., vol. 305, pp. 269–284, 2015.

    Article  MathSciNet  Google Scholar 

  11. J. Chen, C. Richard, and A. H. Sayed, “Multitask diffusion adaptation over networks,” IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4129–4144, 2014.

    Article  MathSciNet  Google Scholar 

  12. J. Garcia-Ojalvo, M. B. Elowitz, S. H. Strogatz, “Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing,” Proc. Natl. Acad. Sci., vol. 101, pp. 10955, 2004.

    Article  MathSciNet  Google Scholar 

  13. E. Ullner, A. Koseska, J. Kurths, E. Volkov, H. Kantz, and J. Garcia-Ojalv, “Multistability of synthetic genetic networks with repressive cell-to-cell communication,” Phys. Rev. E, vol. 78, pp. 031904, 2008.

    Article  Google Scholar 

  14. C. Altafini, “Consensus problems on networks with antagonistic interactions,” IEEE Tran. Autom. Control, vol. 58, no. 4, pp. 935–946, 2013.

    Article  MathSciNet  Google Scholar 

  15. J. H. Qin and C. B. Yu, “Cluster consensus control of generic linear multi-agent systems under directed topology with acyclic partition,” Automatica, vol. 49, no. 9, pp. 2898–2905, 2013.

    Article  MathSciNet  Google Scholar 

  16. Y. Han, W. L. Lu, and T. P. Chen, “Cluster consensus in discrete-time networks of multiagents with inter-cluster nonidentical inputs,” IEEE Trans. Neural Netw. Learning Syst., vol. 24, no. 4, pp. 566–578, 2013.

    Article  Google Scholar 

  17. Y. Chen, J. Lü, F. L. Han, and X. Yu, “On the cluster consensus of discrete-time multi-agent systems,” Syst. Control Lett., vol. 60, no. 7, pp. 517–523, 2011.

    Article  MathSciNet  Google Scholar 

  18. G. Balazsi, A. Cornell-Bell, A. B. Neiman, and F. Moss, “Synchronization of hyperexcitable systems with phase-repulsive coupling,” Phys. Rev. E, vol. 64, pp. 041912, 2001.

    Article  Google Scholar 

  19. C. G. Cassandras, “The event-driven paradigm for control, communication and optimization,” J. Control Decision, vol. 1, no. 1, pp. 3–17, 2014.

    Article  Google Scholar 

  20. D. V. Dimarogonas, E. Frazzoli, and K. H. Johansson, “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Autom. Control, vol. 57, no. 5, pp. 1291–1297, 2012.

    Article  MathSciNet  Google Scholar 

  21. X. Y. Meng and T. W. Chen, “Event based agreement protocols for multi-agent networks,” Automatica, vol. 49, no. 7, pp. 2125–2132, 2013.

    Article  MathSciNet  Google Scholar 

  22. G. Guo, L. Ding, and Q. L. Han, “A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems,” Automatica, vol. 50, no. 5, pp. 1489–1496, 2014.

    Article  MathSciNet  Google Scholar 

  23. Z. Y. Lin, B. Francis, and M. Maggiore, “State agreement for continuous time coupled nonlinear systems,” SIAM J. Control Optim., vol. 46, no. 1, pp. 288–307, 2007.

    Article  MathSciNet  Google Scholar 

  24. M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proc. Natl. Acad. Sci., vol. 99, no. 12, 7821–7826, 2002.

    Article  MathSciNet  Google Scholar 

  25. Z.-H. Guan, B. Hu, M. Chi, D.-X. He, and X.-M. Cheng, “Guaranteed performance consensus in second-order multi-agent systems with hybrid impulsive control,” Automatica, vol. 50, no. 9, pp. 2415–2418, 2014.

    Article  MathSciNet  Google Scholar 

  26. J. P. Hu and W. X. Zheng, “Bipartite consensus for multi-agent systems on directed signed networks,” in: Proc. 52nd IEEE CDC, 2013, pp. 3451–3456.

    Google Scholar 

  27. S. Mitra, H. Banka, and W. Pedrycz, “Rough-fuzzy collaborative clustering,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 4, pp. 795–805, 2006.

    Article  Google Scholar 

  28. K. Wang, Z. D. Teng and H. J. Jiang, “Adaptive synchronization of neural networks with time-varying delay and distributed delay,” Physica A, vol. 387, no. 2, pp. 631–642, 2008.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Appendix

Appendix

Proof of Lemma 9.2

Proof

It is easy to observe that V (y, z) is nonnegative continuous. The reminder is to verify that \(\frac {d V(y(t),z(t))}{dt}\mid _{\text{(9.12)}}\leq 0\), ∀t ≥ 0.

For t ∈ [kh, (k + 1)h), taking time derivative of V (y(t), z(t)) along with the trajectory of system (9.12) yields

$$\displaystyle \begin{aligned} \begin{array}{rcl} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} &\displaystyle = &\displaystyle y(t)^\top \dot{y}(t)+ z(t)^\top \dot{z}(t) \\ &\displaystyle = &\displaystyle -y(t)^\top \varPhi_1(kh)-z(t)^\top \varPhi_2(kh), \end{array} \end{aligned} $$

where Φ 1(kh) = (L 1 + p 1 D 1)(y(kh) + e y(kh)) + p 1 M 1(z(kh) + e z(kh)), Φ 2(kh) = p 2 M 2(y(kh) + e y(kh)) + (L 2 + p 2 D 2)(z(kh) + e z(kh)). Then it follows

$$\displaystyle \begin{aligned} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} \leq \varOmega_1(kh)+\varOmega_2(kh), \end{aligned} $$
(9.15)

where Ω 1(kh) = −y(kh) Φ 1(kh) +  1(kh) Φ 1(kh), Ω 2(kh) = −z(kh) Φ 2(kh) +  2(kh) Φ 2(kh).

Under the condition \(2h\lambda _{\max }(L_r+p_rD_r)\leq 1\), r = 1, 2, one has

$$\displaystyle \begin{aligned} \begin{array}{rcl} \varOmega_1 (kh) &\displaystyle \leq &\displaystyle -\frac{1}{2}y(kh)^\top (L_1+p_1D_1)y(kh) +\frac{1}{2} e_y(kh)^\top (L_1+p_1D_1) e_y(kh) \\ &\displaystyle &\displaystyle -p_1y(kh)^\top M_1 \big(z(kh)+e_z(kh)\big) \\ &\displaystyle &\displaystyle +p_1 \big(z(kh)+e_z(kh)\big)^\top M_1^\top M_1 \big(z(kh)+e_z(kh)\big) \\ &\displaystyle &\displaystyle +2p_1 \big(y(kh)+e_y(kh)\big)^\top (L_1+p_1D_1)^\top M_1 \big(z(kh)+e_z(kh)\big), \\ \varOmega_2 (kh) &\displaystyle \leq &\displaystyle -\frac{1}{2} z(kh)^\top (L_2+p_2D_2)z(kh) +\frac{1}{2} e_z(kh)^\top (L_2+p_2D_2) e_z(kh) \\ &\displaystyle &\displaystyle -p_2z(kh)^\top M_2 \big(y(kh)+e_y(kh)\big) \\ &\displaystyle &\displaystyle +p_2 \big(y(kh)+e_y(kh)\big)^\top M_2^\top M_2 \big(y(kh)+e_y(kh)\big) \\ &\displaystyle &\displaystyle +2p_2 \big(z(kh)+e_z(kh)\big)^\top (L_2+p_2D_2)^\top M_2 \big(y(kh)+e_y(kh)\big). \end{array} \end{aligned} $$

Hence, the inequality (9.15) implies

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} &\displaystyle &\displaystyle \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} \\ &\displaystyle \leq &\displaystyle -\frac{1}{2}y(kh)^\top (L_1+p_1D_1)y(kh) +\frac{1}{2} e_y(kh)^\top (L_1+p_1D_1) e_y(kh) \\ &\displaystyle &\displaystyle -p_1y(kh)^\top M_1 \big(z(kh)+e_z(kh)\big) \\ &\displaystyle &\displaystyle +p_1 \big(z(kh)+e_z(kh)\big)^\top M_1^\top M_1 \big(z(kh)+e_z(kh)\big) \\ &\displaystyle &\displaystyle +2p_1 \big(y(kh)+e_y(kh)\big)^\top (L_1+p_1D_1)^\top M_1 \big(z(kh)+e_z(kh)\big) \\ &\displaystyle &\displaystyle -\frac{1}{2} z(kh)^\top (L_2+p_2D_2)z(kh) +\frac{1}{2} e_z(kh)^\top (L_2+p_2D_2) e_z(kh) \\ &\displaystyle &\displaystyle -p_2z(kh)^\top M_2 \big(y(kh)+e_y(kh)\big) \\ &\displaystyle &\displaystyle +p_2 \big(y(kh)+e_y(kh)\big)^\top M_2^\top M_2 \big(y(kh)+e_y(kh)\big) \\ &\displaystyle &\displaystyle +2p_2 \big(z(kh)+e_z(kh)\big)^\top (L_2+p_2D_2)^\top M_2 \big(y(kh)+e_y(kh)\big). \end{array} \end{aligned} $$
(9.16)

Next, according to the repulsion mechanism given in Definition 9.3, we discuss the relationship (9.16) from the following two cases. To the point, the first one is devoted to clarifying \(\frac {d V(y(t),z(t))}{dt}\mid _{\text{(9.12)}}\leq 0\) in the case that there exists at least one extra-subgroup link, while the other is to deal with the case that all extra-subgroup links are reset to 0.

A contradiction is carried out first. Suppose there exists a large enough real number T 0 > 0 such that the rule (9.8) holds for \(t_k^i>T_0\), namely there exist at least one pair of link (i 1, i 2), i 1 ∈ N 1, i 2 ∈ N 2, such that \(|x_{i_1}(t_k^{i_1i_2})-x_{i_2}(t_k^{i_1i_2})|\) is lower bounded by a positive threshold. This hypothesis contradicts with the fact that MAN (9.1) reach consensus under the updating rule (9.9) if the network topology G is connected and remains unchanged.

Case 1 Assume there exists at least one linked pair (i 1, i 2), i 1 ∈ V 1, i 2 ∈ V 2, and a large enough real number T 1 > 0 such that the relationship (9.8) holds for \(t_k^{i_1i_2} \leq T_1\), then it follows for kh ∈ [0, T 1) that

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right)^\top \left( \begin{array}{cc} D_1 & M_1 \\ M_2 & D_2 \\ \end{array} \right) \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right) \geq \sigma_1 y(kh)^\top L_1y(kh) \\ \quad +\sigma_2 z(kh)^\top L_2z(kh). \end{array} \end{aligned} $$
(9.17)

Based on the updating rule (9.9), one can get

$$\displaystyle \begin{aligned} \begin{array}{rcl} \varDelta V &\displaystyle = &\displaystyle V(y((k+1)h),z((k+1)h))-V(y(kh),z(kh))\qquad \\ &\displaystyle = &\displaystyle -\frac{1}{2}\parallel y(kh)\parallel^2+\frac{1}{2}\parallel y(kh)-h(L_1+p_1D_1)y(kh)-h p_1M_1z(kh)\parallel^2 \\ &\displaystyle &\displaystyle -\frac{1}{2}\parallel z(kh)\parallel^2 +\frac{1}{2}\parallel z(kh)-h p_2M_2y(kh)-h(L_2+p_2D_2)z(kh)\parallel^2. \end{array} \end{aligned} $$

Then combining with the inequality (9.17), one has

$$\displaystyle \begin{aligned} \begin{array}{rcl} \varDelta V &\displaystyle \leq &\displaystyle -h y(kh)^\top L_1y(kh)-hz(kh)^\top L_2z(kh) \\ &\displaystyle &\displaystyle -h\left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right)^\top \left( \begin{array}{cc} p_1D_1 &\displaystyle p_1M_1 \\ p_2M_2 &\displaystyle p_2D_2 \\ \end{array} \right) \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right) \\ &\displaystyle &\displaystyle + h^2 \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right)^\top \bigg[ \left( \begin{array}{cc} L_1^2 &\displaystyle 0 \\ 0 &\displaystyle L_2^2 \\ \end{array} \right)+ \left( \begin{array}{cc} p_1D_1 &\displaystyle p_1M_1 \\ p_2M_2 &\displaystyle p_2D_2 \\ \end{array} \right)^2 \bigg]\left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right), \end{array} \end{aligned} $$

which, again with the condition \(2h\lambda _{\max }(L_r+p_rD_r)\leq 1\) (r = 1, 2), implies

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} \varDelta V \leq -h\big(\frac{1}{2}+\min(p_1,p_2)\sigma_1-\frac{p_1+p_2}{2}\big)y(kh)^\top L_1y(kh) \\ -h\big(\frac{1}{2}+\min(p_1,p_2)\sigma_2-\frac{p_1+p_2}{2}\big)z(kh)^\top L_2z(kh). \end{array} \end{aligned} $$
(9.18)

According to the ZOH rule, it follows

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} \leq -\big(\frac{1}{2}+\min(p_1,p_2)\sigma_1-\frac{p_1+p_2}{2}\big)y(kh)^\top L_1y(kh) \\ -\big(\frac{1}{2}+\min(p_1,p_2)\sigma_2-\frac{p_1+p_2}{2}\big)z(kh)^\top L_2z(kh).\\ \end{array} \end{aligned} $$
(9.19)

Since \(\sigma _1,\sigma _2\geq \frac {p_1+p_2-1}{2\min (p_1,p_2)}\) and , (9.19) ensures \(\frac {d V(y(t),z(t))}{dt}\mid _{\text{(9.12)}} \leq ~0\).

Case 2 Contrarily, when \(t_k^i>T_1\), the relationship (9.8) breaks for all i 1 ∈ V 1, i 2 ∈ V 2. Namely, \(a_{i_1i_2}(t_k^{i_1i_2})\) is reset to 0, which means M 1 = 0, M 2 = 0, D 1 = 0, D 2 = 0. Then the relationship (9.16) gives rise to

$$\displaystyle \begin{aligned} \begin{array}{rcl} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} &\displaystyle \leq &\displaystyle -\frac{1}{2}y(kh)^\top L_1y(kh)+\frac{1}{2} e_y(kh)^\top L_1e_y(kh) \\ &\displaystyle &\displaystyle -\frac{1}{2} z(kh)^\top L_2z(kh)+\frac{1}{2} e_z(kh)^\top L_2e_z(kh). \end{array} \end{aligned} $$

Substituting the inequality (9.13), it follows

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} & \leq & -\frac{1}{2}(1-\alpha_1 \lambda_{\max}(L_1+p_1D_1))y(kh)^\top L_1y(kh) \\ && -\frac{1}{2} (1-\alpha_2 \lambda_{\max}(L_2+p_2D_2)) z(kh)^\top L_2z(kh) \\ && -\frac{1}{2} \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right)^\top \varTheta \left( \begin{array}{c} y(kh) \\ z(kh) \\ \end{array} \right), \end{array} \end{aligned} $$
(9.20)

where \(\varTheta =\left ( \begin {array}{cc} p_1D_1 & 0 \\ 0 & p_2D_2 \\ \end {array} \right ) -\sum _{r=1}^2 \alpha _r\lambda _{\max }(L_r+p_rD_r)\left ( \begin {array}{cc} D_1 & 0 \\ 0 & D_2 \\ \end {array} \right )\).

Noting that \(\alpha _r \leq \frac {\min (p_1,p_2)}{2\lambda _{\max }(L_r+p_rD_r)}\), r = 1, 2, one has \(\sum _{r=1}^2 \alpha _r\lambda _{\max }(L_r+p_rD_r)\leq \min (p_1,p_2)\), which implies Θ ≥ 0. Then from (9.20) one can get

$$\displaystyle \begin{aligned} \begin{array}{rcl}{} \frac{d V(y(t),z(t))}{dt}\mid_{\text{(9.12)}} &\displaystyle \leq &\displaystyle -\frac{1}{2}\big(1-\frac{\min(p_1,p_2)}{2}\big) y(kh)^\top L_1y(kh) \\ &\displaystyle &\displaystyle -\frac{1}{2} \big(1-\frac{\min(p_1,p_2)}{2}\big) z(kh)^\top L_2z(kh), \end{array} \end{aligned} $$
(9.21)

which with 0 < p 1, p 2 < 2 ensures \(\frac {d V(y(t),z(t))}{dt}\mid _{\text{(9.12)}}\leq 0\).

Therefore, combining Case 1 and Case 2, it follows \(\frac {d V(y(t),z(t))}{dt}\mid _{\text{(9.12)}}\leq 0\), ∀t ≥ 0, in the sense that the relationships (9.19) and (9.21) hold. This completes the proof. □

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Guan, ZH., Hu, B., Shen, X.(. (2019). Event-Driven Communication and Control in Multi-Agent Networks. In: Introduction to Hybrid Intelligent Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-02161-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02161-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02160-3

  • Online ISBN: 978-3-030-02161-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics