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SCIENTIA SINICA Informationis, Volume 46, Issue 11: 1648-1661(2016) https://doi.org/10.1360/N112016-00161

Distributed consensus over digital noisy channel through reliable communications

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  • ReceivedJun 30, 2016
  • AcceptedAug 28, 2016
  • PublishedNov 8, 2016

Abstract

This paper presents an investigation of the distributed consensus problem of multi-agent systems over a digital noisy channel. In digital networks, noise in the digital channel may significantly affect the consensus of a multi-agent system. To overcome the uncertainty of a binary erasure channel, dynamic reliable communication schemes are adopted to ensure that the real-valued random signal is transmitted up to the expected precision. We proposed consensus protocols, under which the quantizer is not saturated and the system is proved to reach consensus if an appropriate communication time is chosen. The complexity of communication is analyzed and a numerical comparison between reliable communication and quantized communication is presented.


Funded by

国家重点基础研究发展计划(973)

(2014CB845301)


References

[1] Qu Z. Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles. Berlin: Springer Science & Business Media, 2009. Google Scholar

[2] Ren W, Cao Y. Distributed Coordination of Multi-Agent Networks: Emergent Problems, Models, and Issues. Berlin: Springer Science & Business Media, 2010. Google Scholar

[3] Olfati Saber R. Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Contr, 2006, 51: 401-420 CrossRef Google Scholar

[4] Fax J A, Murray R M. Information flow and cooperative control of vehicle formations. IEEE Trans Autom Contr, 2004, 49: 1465-1476 CrossRef Google Scholar

[5] Lynch N A. Distributed Algorithms. San Francisco: Morgan Kaufmann, 1996. Google Scholar

[6] Ren W, Beard R, Kingston D. Multi-agent Kalman consensus with relative uncertainty. In: Proceedings of the American Control Conference, Portland, 2005. 1865-1870. Google Scholar

[7] Xiao L, Boyd S, Lall S. A scheme for robust distributed sensor fusion based on average consensus. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, Boise, 2005. 63-70. Google Scholar

[8] Li T, Meng Y, Zhang J F. An overview on quantized consensus and consensus with limited data rate of multi-agent systems. Acta Autom Sin, 2013, 39: 1805-1811 [李韬, 孟扬, 张纪峰. 多自主体量化趋同与有限数据率趋同综述. 自动化学报, 2013, 39: 1805-1811]. Google Scholar

[9] Carli R, Fagnani F, Frasca P. Average consensus on networks with transmission noise or quantization. In: Proceedings of European Control Conference, Kos, 2007. 1852-1857. Google Scholar

[10] Carli R, Fagnani F, Frasca P, et al. A probabilistic analysis of the average consensus algorithm with quantized communication. In: Proceedings of the 17th International Federation of Automatic Control (IFAC) World Congress, Seoul, 2008. 41: 8062-8067. Google Scholar

[11] Fagnani F, Carli R, Frasca P, et al. Average consensus on networks with quantized communication. Int J Robust Nonlin Contr, 2009, 19: 1787-1816 CrossRef Google Scholar

[12] Huang M. Stochastic consensus seeking with noisy and directed inter-agent communication: fixed and randomly varying topologies. IEEE Trans Autom Contr, 2010, 55: 235-241 CrossRef Google Scholar

[13] Li T, Zhang J F. Consensus conditions of multi-agent systems with time-varying topologies and stochastic communication noises. IEEE Trans Autom Contr, 2010, 55: 2043-2057 CrossRef Google Scholar

[14] Carli R, Fagnani F. Communication constraints in the average consensus problem. Automatica, 2008, 44: 671-684 CrossRef Google Scholar

[15] Liu S, Li T, Xie L H, et al. Continuous-time and sampled-data-based average consensus with logarithmic quantizers. Automatica, 2013, 49: 3329-3336 CrossRef Google Scholar

[16] Carli R, Bullo F, Zampieri S. Quantized average consensus via dynamic coding/decoding schemes. Int J Robust Nonlin Contr, 2010, 20: 156-175 CrossRef Google Scholar

[17] Li T, Min Y F, Xie L H, et al. Distributed consensus with limited communication data rate. IEEE Trans Autom Contr, 2011, 56: 279-292 CrossRef Google Scholar

[18] You K Y, Xie L H. Network topology and communication data rate for consensusability of discrete-time multi-agent systems. IEEE Trans Autom Contr, 2011, 56: 2262-2275 CrossRef 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 CrossRef Google Scholar

[20] Zhang Q, Zhang J F. Quantized data based distributed consensus under directed time-varying communication topology. SIAM J Contr Optim, 2013, 51: 332-352 CrossRef Google Scholar

[21] Liu S, Li T, Xie L H. Distributed consensus for multiagent systems with communication delays and limited data rate. SIAM J Contr Optim, 2011, 49: 2239-2262 CrossRef Google Scholar

[22] Como G, Fagnani F, Zampieri S. Anytime reliable transmission of real-valued information through digital noisy channels. SIAM J Contr Optim, 2010, 48: 3903-3924 CrossRef Google Scholar

[23] Carli R, Como G, Fagnani F. Distributed averaging on digital erasure networks. Automatica, 2011, 47: 115-121 CrossRef Google Scholar

[24] Shirazinia A, Zaidi A, Lei B. Dynamic source-channel coding for estimation and control over binary symmetric channels. IET Contr Theory Appl, 2015, 9: 1444-1454 CrossRef Google Scholar

[25] Gersho A, Gray R M. Vector Quantization and Signal Compression. Berlin: Springer Science & Business Media, 2012. Google Scholar

[26] Chow Y S, Teicher H. Probability Theory: Independence, Interchangeability, Martingales. Berlin: Springer Science & Business Media, 2012. Google Scholar

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