Wireless Personal Communications

, Volume 99, Issue 4, pp 1423–1441 | Cite as

A Novel Approach for Fast Average Consensus Under Unreliable Communication in Distributed Multi Agent Networks

  • Ali Mustafa
  • Muhammad Najam ul Islam
  • Salman Ahmed
  • Muhammad Ahsan Tufail
Article
  • 108 Downloads

Abstract

In this research an algorithm is proposed to find the total number of agents participating in a multi agent network. Also to achieve hasten distributed average consensus in order to consider a network with reliable and unreliable communication links. Class of algorithm is considered in which fixed initial state values are assigned to all agents in the network, with the iterations they updates their initial values by communicating with their neighboring agents within a multi agent network. Algorithm with weighted matrix satisfy the convergence condition of average consensus and accelerate the method to achieve the consensus. Usually this convergence process is relatively sluggish and take moreover numerous iterations to achieve a consensus. To overcome the above issues, a new approach is proposed in order to minimize the rate of convergence. A two step algorithm has been proposed, where in step one each agent employs a linear predictor to predict future agent values. In second step the computed values are used to proceed further by the other agents to achieve consensus in order to bypass the redundant states. In the end proposed algorithm is compared with other existing consensus frameworks to strengthen the claim regarding the proposed two step algorithm which leads to escalate the rate of convergence and reduces the number of iterations.

Keywords

Multi agent systems Unreliable communication Distributed estimation and consensus control 

References

  1. 1.
    Kahani, M., & Beadle, H. (1997). Decentralized approaches for network management. Computer Communications, 27, 36–47.CrossRefGoogle Scholar
  2. 2.
    Demers, A., Greene, D., Hauser, C., Irish, W., Larson, L., Shenker, S., Sturgis, H., Swinehart, D., & Terry, D. (1987). Epidemic algorithms for replicated database maintenance. In Proceedings of 6th annual ACM symposium on principles of distributed computing (pp. 1–12).Google Scholar
  3. 3.
    Evers, J., Kiss, D., Kowalczyk, W., Navilarekallu, T., Renger, A. L. S. M., Timperio, A. A. V. V., Wijk, S. V., & Yzelman, A. J. (2011). Node counting in wireless ad-hoc networks. In Proceedings of the 79th European study group mathematics with industry (pp. 49–73).Google Scholar
  4. 4.
    Perkins, C. E., & Royer, E. M. (1999). Adhoc on-demand distance vector routing. In Second IEEE workshop on mobile computing systems and applications (pp. 90–100).Google Scholar
  5. 5.
    Rehan, S., & Bansal, M. (2013). Performance comparison among different evolutionary algorithms in terms of node count reduction in bdds. International Journal of VLSI and Embedded Systems, 4, 491–496.Google Scholar
  6. 6.
    Mustafa, A., Ahmed, S., Islam, N., & Tufail, A. (2016). An efficient agent scheming in distributed time varying networks. In In proceedings of SAI intelligent systems conference (pp. 804–809).Google Scholar
  7. 7.
    Vicsek, T., Czirok, A., Benjacob, E., Cohen, I., et al. (1995). Novel type of phasetransition in a system of self-driven particles. Physical Review Letters, 75(6), 122–1229.CrossRefGoogle Scholar
  8. 8.
    Yu, H., Xia, X., & Zhang, T. (2011). A less conservative method for average consensus with multiple time-varying delays. IEEE Africon, 1–6.Google Scholar
  9. 9.
    Borkar, V., & Varaiya, P. (1982). Asymptotic agreement in distributed estimation. IEEE Transactions on Automatic Control, 27(3), 650–655.MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Tsitsiklis, J. (1984). Problems in decentralized decision making and computation. Ph.D. Thesis, Massachusetts nstit. Technology, Cambridge.Google Scholar
  11. 11.
    Fax., J. (2001). Optimal and cooperative control of vehicle formations. Ph.D. Thesis, Control Dynamical Syst., California Inst. Technol., Pasadena, CA.Google Scholar
  12. 12.
    Moreau, L. (2005). Stability of multi-agent systems with time-dependent communication links. IEEE Transactions on Automatic Control, 50, 169–182.MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Moreau, L. (2004). Stability of continuous-time distributed consensus algorithms. In Proceedings of the 43rd IEEE conference on decision and control (pp. 3998–4003).Google Scholar
  14. 14.
    Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on Automatic Control, 51(3), 401–420.MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Saber, R. O., & Murray, R. M. (2004). Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 49(9), 1520–1533.MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Ren, W., & Beard, R. W. (2005). Consensus seeking in multi agent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5), 655–661.MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Xiao, L., Boyd, S., & Lall, S. (2005). A scheme for robust distributed sensor fusion based on average consensus. In Proceedings of IEEE/ACM international symposium on information processing in sensor networks (pp. 63–70).Google Scholar
  18. 18.
    Xiao, L., Boyd, S., & Kim, S. (2007). Distributed average consensus with least-mean-square deviation. Journal of Parallel and Distributed Computing, 67(1), 33–46.CrossRefMATHGoogle Scholar
  19. 19.
    Aysal, T. C., Oreshkin, B. N., & Coates, M. J. (2009). Accelerated distributed average consensus via localized node state prediction. IEEE Transactions on Signal Processing, 57(4), 1563–1576.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Saber, R. O., & Murray, R. M. (2003). Consensus protocols for networks of dynamic agents. In Proceedings of the American control conference.Google Scholar
  21. 21.
    Hu, H. X., Yu, L., Zhang, W. A., & Song, H. (2013). Group consensus in multi-agent systems with hybrid protocol. Journal of the Franklin Institute, 350(3), 575–597.MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Xiao, L., & Boyd, S. (2004). Fast linear iterations for distributed averaging. System and Control Letters, 53(1), 65–78.MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Boyd, S., Ghosh, A., Prabhakar, B., & Shah, D. (2006). Randomized gossip algorithms. IEEE Transaction of Information Theory, 52(6), 2508–2530.MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Sundaram, S., & Hadjicostis, C. (2007). Distributed consensus and linear function calculation in networks: An observability perspective. In Proceedings of IEEE/ACM of international symposium of information processing sensor networks (pp. 99–108).Google Scholar
  25. 25.
    Wang, T., Qiu, J., Gao, H., & Wang, C. (2016). Network-based fuzzy control for nonlinear industrial processes with predictive compensation strategy. In IEEE transaction on systems, man, and cybernetics: systems (pp. 1–11).Google Scholar
  26. 26.
    Wang, T., Qiu, J., & Gao, H. (2016). Adaptive neural control of stochastic nonlinear time-delay systems with multiple constraint. In IEEE transaction on systems, man, and cybernetics: systems (pp. 1–9).Google Scholar
  27. 27.
    Wang, T., Huijun Gao, F., & Qiu, J. (2016). A combined fault-tolerant and predictive control for network-based industrial processes. IEEE Transaction on Industrial Electronics, 63(4), 2529–2536.Google Scholar
  28. 28.
    Wang, T., Gao, H., & Qiu, J. (2016). A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Transaction on Neural Networks and Learning Systems, 27(2), 416–425.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Qiu, J., Ding, S. X., Gao, H., & Yin, S. (2016). Fuzzy-model-based reliable static output feedback h control of nonlinear hyperbolic PDE system. IEEE Transaction on Fuzzy Systems, 24(2), 388–400.CrossRefGoogle Scholar
  30. 30.
    Cao, M., Spielman, D. A., & Yeh, E. M. (2006). Accelerated gossip algorithms for distributed computation. In Proceedings of 44th annual conference of communication, control and computation (pp. 952–959).Google Scholar
  31. 31.
    Feng, Y., Xu, S., & Zhang, B. (2014). Group consensus control for double integrator dynamic multiagent systems with fixed communication topology. International Journal of Robust and Nonlinear Control, 24(3), 532–547.MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Ren, W.,. Beard, R. W., & Atkins, E. M. (2005). A survey of consensus problems in multi-agent coordination. In American control conference (pp. 1859–1864).Google Scholar
  33. 33.
    Gantmacher, F. R. (1959). Matrix theory (Vol. 2). New York: Chelsea.MATHGoogle Scholar
  34. 34.
    Meng, X., & Chen, T. (2013). Event-based agreement protocols for multi-agent networks. Automatica, 49(1), 2125–2132.MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Wei, Y., & Lin, Z. (2015) On the delay bounds of linear systems under delay independent truncated predictor feedback: The state feedback case. In 54th annual IEEE conference on decision and control.Google Scholar
  36. 36.
    Xiao, L., & Boyd, S. (2003). Fast linear iterations for distributed averaging. In Proceedings of of 42nd IEEE conference on decision and control, 5, pp. 4997–5002.Google Scholar
  37. 37.
    Merris, R. (1994). Laplacian matrices of graphs: A survey. Linear Algebra Application, 197, 143–176.MathSciNetCrossRefMATHGoogle Scholar
  38. 38.
    Kar, S., & Moura, J. (2008). Sensor networks with random links: Topology design for distributed consensus. IEEE Transactions on Signal Processing, 56, 3315–3326.MathSciNetCrossRefGoogle Scholar
  39. 39.
    Kokiopoulou, E., & Frossard, P. (2009). Polynomial filtering for fast convergence in distributed consensus. IEEE Transactions on Signal Processing, 57, 342–354.MathSciNetCrossRefGoogle Scholar
  40. 40.
    Hastings, W. (1970). Monte carlo sampling methods using markov chains and their applications. Biometrika, 57, 97–109.MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ali Mustafa
    • 1
    • 2
  • Muhammad Najam ul Islam
    • 1
  • Salman Ahmed
    • 3
  • Muhammad Ahsan Tufail
    • 2
  1. 1.Department of Electrical EngineeringBahria UniversityIslamabadPakistan
  2. 2.Department of Electrical EngineeringCOMSATS Institute of ITAttockPakistan
  3. 3.Department of Computer System EngineeringUniversity of Engineering and TechnologyPeshawarPakistan

Personalised recommendations