Skip to main content
SpringerLink
Log in

Consensus of switched multi-agent systems with binary-valued communications

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

This paper studies the consensus of switched multi-agent systems (MAS) with binary-valued communications. Different from the existing studies on switched MAS considering precise observations, each agent studied in this research only receives binary-valued information with stochastic noises from its neighbors’ states. Further, unlike the existing studies on MAS with binary-valued information in a fixed topology, in this paper, we consider the jointly connected undirected graphs, each of which switches with non-zero probability. The consensus algorithm comprises of two stages: first, the connected agents employ a recursive projection algorithm to estimate their neighbors’ states based on the binary-valued communications; second, the control law of the connected agents is developed based on the estimations to upgrade their states. It is proved that both the speed of the estimation convergence to the real states and the consensus speed of the states can achieve O(1/t) when the iteration step is given a proper value. Furthermore, the results indicate that the larger the value of the lowest probability that a graph emerges with, the more easily the consensus could be achieved. Finally, a simulation is presented to demonstrate the theoretical analysis.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Su H, Wang X, Yang W. Flocking in multi-agent systems with multiple virtual leaders. Asian J Control, 2008, 10: 238–245

    Article  MathSciNet  Google Scholar 

  2. Chen Z, Huang Y, Li D, et al. Distributed adjacency weight design for second-order consensus in wireless sensor and actuator networks. Asian J Control, 2017, 19: 1365–1374

    Article  MathSciNet  MATH  Google Scholar 

  3. Kar S, Moura J M F. Distributed consensus algorithms in sensor networks: quantized data and random link failures. IEEE Trans Signal Process, 2010, 58: 1383–1400

    Article  MathSciNet  MATH  Google Scholar 

  4. Bhatta P, Leonard N E. Stabilization and coordination of underwater gliders. In: Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, 2002. 2081–2086

  5. Blondel V D, Hendrickx J M, Olshevsky A, et al. Convergence in multiagent coordination, consensus, and flocking. In: Proceedings of the 44th IEEE Conference on Decision and Control, Seville, 2005. 2996–3000

  6. Li T, Zhang J F. Mean square average-consensus under measurement noises and fixed topologies: necessary and sufficient conditions. Automatica, 2009, 45: 1929–1936

    Article  MathSciNet  MATH  Google Scholar 

  7. Lu K H, Jing G S, Wang L. Distributed algorithms for solving the convex feasibility problems. Sci China Inf Sci, 2020, 63: 189201

    Article  Google Scholar 

  8. Duan G P, Xiao F, Wang L. Hybrid event- and time-triggered control for double-integrator heterogeneous networks. Sci China Inf Sci, 2019, 62: 022203

    Article  MathSciNet  Google Scholar 

  9. Wang T, Zhao Y L. Identification, control and application of set-valued system. J Nanjing Univ Inf Tech Sci Edition, 2017, 9: 319–325

    MATH  Google Scholar 

  10. Jing L D, Zhang J F. Tracking control and parameter identification with quantized ARMAX systems. Sci China Inf Sci, 2019, 62: 199203

    Article  MathSciNet  Google Scholar 

  11. Wang T, Zhang H, Zhao Y. Average consensus of multi-agent systems under directed topologies and binary-valued communications. IEEE Access, 2018, 6: 55995–56006

    Article  Google Scholar 

  12. Zhao Y, Wang T, Bi W. Consensus protocol for multiagent systems with undirected topologies and binary-valued communications. IEEE Trans Automat Contr, 2019, 64: 206–221

    Article  MathSciNet  MATH  Google Scholar 

  13. Wang T, Zhao Y, Wang X. Consensus algorithm of multi-agent system with binary-valued communication. In: Proceedings of the 10th Asian Control Conference, Sabah, 2015. 1–6

  14. Guo J, Zhao Y. Recursive projection algorithm on FIR system identification with binary-valued observations. Automatica, 2013, 49: 3396–3401

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang T, Hu M, Zhao Y. Convergence properties of recursive projection algorithm for system identification with binary-valued observations. In: Proceedings of the China Automation Congress, Xi’an, 2018. 2961–2966

  16. Wang T, Zhang H, Zhao Y. Consensus of multi-agent systems under binary-valued measurements and recursive projection algorithm. IEEE Trans Automat Contr, 2020, 65: 2678–2685

    Article  MathSciNet  MATH  Google Scholar 

  17. Lee T C, Xia W, Su Y, et al. Exponential consensus of discrete-time systems based on a novel Krasovskii-LaSalle theorem under directed switching networks. Automatica, 2018, 97: 189–199

    Article  MathSciNet  MATH  Google Scholar 

  18. Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Automat Contr, 2003, 48: 988–1001

    Article  MathSciNet  MATH  Google Scholar 

  19. Li T, Xie L H. Distributed consensus over digital networks with limited bandwidth and time-varying topologies. Automatica, 2011, 47: 2006–2015

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhang Q, Zhang J F. Quantized data-based distributed consensus under directed time-varying communication topology. SIAM J Control Optim, 2013, 51: 332–352

    Article  MathSciNet  MATH  Google Scholar 

  21. Li D Q, Liu Q, Wang X, et al. Quantized consensus over directed networks with switching topologies. Syst Control Lett, 2014, 65: 13–22

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang X D. Matrix Analysis and Applications. 2nd ed. Beijing: Tsinghua University Press, 2013

    Google Scholar 

  23. Huang M, Manton J H. Stochastic Lyapunov analysis for consensus algorithms with noisy measurements. In: Proceedings of the American Control Conference, New York, 2007. 1419–1424

  24. Evans L C. An Introduction to Stochastic Differential Equations. Providence: American Mathematical Society, 2012. 82

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Key R&D Program of China (Grant No. 2018YFA0703800), National Natural Science Foundation of China (Grant Nos. 61803370, 61622309), and China Postdoctoral Science Foundation (Grant No. 2018M630216).

Author information

Authors and Affiliations

  1. The Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Min Hu, Ting Wang & Yanlong Zhao

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

    Min Hu, Ting Wang & Yanlong Zhao

Authors
  1. Min Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanlong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanlong Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, M., Wang, T. & Zhao, Y. Consensus of switched multi-agent systems with binary-valued communications. Sci. China Inf. Sci. 65, 162207 (2022). https://doi.org/10.1007/s11432-020-3052-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3052-0

Keywords

Search

Navigation