Application Layer Control System: Consensus-Based Control, Theoretical Results and Performance Issues

  • Sabato ManfrediEmail author
Part of the Advances in Industrial Control book series (AIC)


In this chapter, we formulate consensus-based algorithms operating at the application layer of the multilayer control system in Fig.  1.2. The proposed algorithms deal with the physical variables of the NCPS and can be used for monitoring and control purposes. Sufficient conditions for the application layer control system stability are presented and used for algorithm design. Performance and implementation issues are also remarked. Finally, design methodology of the overall multilayer control system in Fig.  1.2 is pointed out.


Convergence Speed Network Control System Laplacian Matrix Consensus Algorithm Packet Collision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Xiao, L., Boyd, S.: Fast linear iterations for distributed averaging. Syst. Control Lett. 52, 65–78 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Olfati-Saber, R.: Ultrafast consensus in small-world networks. Proceedings of the 2005 American Control Conference, vol. 4, pp. 2371–2378 (2005)Google Scholar
  3. 3.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  4. 4.
    Manfredi, S., Di Bernardo, M., Garofalo, F.: Small world effects in networks: an engineering interpretation. Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 4, pp. IV- 820-3 (2004)Google Scholar
  5. 5.
    Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 52, 2508–2530 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Wu, Z., Fang, H.: Delayed-state-derivative feedback for improving consensus performance of second-order delayed multi-agent systems. Int. J. Syst. Sci. 43, 140–152 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Li, J., Xu, S., Chu, Y., Wang, H.: Distributed average consensus control in networks of agents using outdated states. IET Control Theory Appl. 4(5), 746 (2010)Google Scholar
  8. 8.
    Jin, Z., Murray, R.M.: Multi-hop relay algorithms for fast consensus seeking. IEEE Conference on Decision and Control, San Diego (2006)Google Scholar
  9. 9.
    Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Gossip algorithms: design, analysis, and applications. Proceedings of IEEE INFOCOM, Miami, vol. 3, pp. 1653–1664 (2005)Google Scholar
  10. 10.
    Nedic, A., Ozdaglar, A.: Distributed subgradient methods for multi-agent optimization. IEEE Trans. Autom. Control 54, 48–61 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Tsitsiklis, J.: Problems in decentralized decision making and computation. Ph.D. dissertation, Lab. Information and Decision (1984)Google Scholar
  12. 12.
    Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. 44th Annual IEEE Symposium on Foundations of Computer Science, Washington (2003)Google Scholar
  13. 13.
    Spanos, D.P., Saber, R.O., Murray, R.M.: Dynamic consensus on mobile networks. IFAC World Congress (2005)Google Scholar
  14. 14.
    Industrial Wireless Technology for the: 21st Century. Report, Technology, Foresight (2004)Google Scholar
  15. 15.
    Dong, M.J., Yung, G., Kaiser, W. J.: Low power signal processing architectures for network microsensors. Proceedings of International Symposia on Low Power Electronics and Design (1997).
  16. 16.
    Kahn, J.M., Katz, R.H., Pister, K.S.J.: Mobile networking for smart dust. Proceedings of ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM) (1999)Google Scholar
  17. 17.
    Rabaey, J., Ammer, J., Karalar, T., Li, S., Otis, B., Sheets, M., Tuan, T.: PicoRadios for wireless sensor networks: the next challenge in ultra-low-power design. Proceedings of the Inernational Solid-State Circuits Conference (2002)Google Scholar
  18. 18.
    Heinzelman, W., Chandrakasan, A.P., Balakrishnan, H.: An application-specific algorithm architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 2, 660–670 (2002)CrossRefGoogle Scholar
  19. 19.
    Yu, Y., Prasanna, V.K., Hong, B.: Communication models for algorithm design in networked sensor systems. 19th International Parallel and Distributed Processing Symposium, Denver (2005)Google Scholar
  20. 20.
    Sgroi, M., Wolisz, A., Sangiovanni-Vincentelli, A., Rabaey J.M.: A Service-Based Universal Application Interface for Ad-hoc Wireless Sensor Networks.
  21. 21.
    Bonivento, A., Carloni, L.P., Sangiovanni-Vincentelli, A.L.: Rialto: a bridge between description and implementation of control algorithms for wireless sensor networks. Proceedings of the Fifth International Conference on Embedded Software (EMSOFT) (2005)Google Scholar
  22. 22.
    Heinzelman, W.B., Murphy, A.L., Carvalho, H.S., Perillo, M.A.: Middleware to support sensor network applications. IEEE Netw. 18, 6–14 (2004)CrossRefGoogle Scholar
  23. 23.
    Grime, S., Durrant-Whyte, H.F.: Data fusion in decentralized sensor networks. Control Eng. Pract. 2, 849–863 (1994)CrossRefGoogle Scholar
  24. 24.
    Neumann, P.: Communication in industrial automation-what is going on? Control Eng. Pract. 15, 1332–1347 (2007)CrossRefGoogle Scholar
  25. 25.
    Moreau, L.: Stability of multi-agent systems with time-dependent communication links. IEEE Trans. Autom. Control 50, 169–182 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Ren, W., Beard, R.W.: Consensus seeking in multi-agent systems under dynamically changing interaction topologies. IEEE Trans. Autom. Control 50, 655–661 (2005)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48, 988–1001 (2003)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Saber, R.O., Murray, R.M.: Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49, 1520–1533 (2004)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1985)Google Scholar
  30. 30.
    Horn, R.A., Johnson C.R.: Topics in Matrix Analysis. Cambridge University Press, Cambridge (1985)Google Scholar
  31. 31.
    Desoer, C.A., Yang, Y.T.: On the generalized Nyquist stability criterion. IEEE Trans. Autom. Control 25, 187–196 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Wu, C.W.: On bounds of extremal eigenvalues of irreducible and m-reducible matrices. linear Algebra Appl. 402, 29–45 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Manfredi, S., Angeli, D.: Frozen state conditions for exponential consensus of time-varying cooperative nonlinear networks. Automatica. 64, 182–189 (2016)Google Scholar
  34. 34.
    Manfredi, S.: Robust scalable stabilizability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: toward a distributed computing architecture. Int. J. Control 89, 6, 1203–1213 (2016)Google Scholar
  35. 35.
    Manfredi, S., Angeli, D.: Asymptotic consensus of time-varying nonlinear non-autonomous networks under almost periodic integral connectivity: an application to autonomous sampling by mobile sensor nodes. IEEE 54th Annual Conference on Decision and Control (CDC) (2016)Google Scholar
  36. 36.
    Manfredi, S., Angeli, D.: On exponential consensus of time-varying non-cooperative nonlinear networks. European Control Conference (ECC) (2015)Google Scholar
  37. 37.
    Manfredi, S.: On Global and Local Consensusability of Multi-Agent Systems with Input Constraint and Uncertain Initial Conditions. American Control Conference (2013)Google Scholar
  38. 38.
    Manfredi, S., Angeli, D.: Frozen state conditions for asymptotic consensus of time-varying cooperative nonlinear networks. IEEE 52nd Conference on Decision and Control (2013)Google Scholar
  39. 39.
    Manfredi, S.: Consensuability Conditions of Multi Agent Systems with Varying Interconnection Topology and Different Kinds of Node Dynamics. In: Bartoszewics, A., Robust Control, Theory and Applications, pp. 423–440, INTECH Publishing (2011)Google Scholar
  40. 40.
  41. 41.
    Zhang, Y., Gulliver, T.A.: Quality of service for ad hoc on-demand distance vector routing. IEEE Int. Conf. Wirel. Mobile Comput. 3, 192–196 (2005)Google Scholar
  42. 42.
    Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Sel. Areas Commun. 18, 535–547 (2000)CrossRefGoogle Scholar
  43. 43.
    Kumar, A., Altman, E., Miorandi, D., Goyal, M.: New insights from a fixed point analysis of single cell IEEE 802.11 WLANs. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1550–1561 (2005)Google Scholar
  44. 44.
    Pollin, S., Ergen, M., Ergen, S.C., Bougard, B., Van der Perre, L., Catthoor, F., Moerman, I., Bahai, A., Varaiya, P.: Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer. IEEE INFOCOM (2005)Google Scholar
  45. 45.
    Rao, V.P., Marandin, D.: Adaptive Backoff Exponent Algorithm for Zigbee (IEEE 802.15.4). NEW2AN (2006)Google Scholar
  46. 46.
    Hull, B., Jamieson, K., Balakrishnan, H.: Mitigating Congestion in Wireless Sensor Networks. ACM SenSys 2004, Baltimore (2004)Google Scholar
  47. 47.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information TechnologyUniversity of Naples Federico IINaplesItaly

Personalised recommendations