159
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

Quantised consensus by using the PD-like protocols in directed networks

, &
Pages 1576-1583 | Received 16 Nov 2015, Accepted 16 Jul 2016, Published online: 11 Aug 2016
 

ABSTRACT

This paper studies the distributed consensus problem in sampled-data multi-agent systems with directed network topologies subject to a quantisation constraint. Different from the widely used consensus protocol which exploits current information, we adopt a distributed proportional-differential (PD)-like protocol. First, we provide a necessary and sufficient condition of control gains which guarantee the consensus with the assumption that real-valued communication information can be obtained. Next, we analyse the quantisation effects in system performances under the proposed protocol; it is proved that the quantised consensus can be achieved. Finally, by providing numerical examples, we show that with appropriate parameters, the consensus can be achieved and the quantisation noises can be attenuated effectively.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

China Postdoctoral Science Foundation [grant number 2014M560438]; National Natural Science Foundation of China [61403211]; Postdoctoral Science Foundation of Jiangsu Province of China [1402065B]; Natural Science Foundation of the Jiangsu Higher Education Institutions of China [14KJB120008].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,709.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.