Co-Regulating Communication for Asynchronous Information Consensus

Chandima Fernando, Carrick Detweiler, Justin Bradley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Muti-agentconsensus controllers typically use discrete communication and hence are restricted to fixed-rate or event-triggered communication. Fixed-rate communication suffers from inefficient use of communication and computational resources but is easy to implement, while event-triggered communication conserves resources but suffers from the ambiguity of all event-triggered systems-inability to distinguish failure from lack of new information. We propose a novel hybrid strategy of co-regulating communication with state disagreement amongst the agents obtaining the benefits of discrete fixed-rate and event-triggered consensus while mitigating the associated disadvantages. Our approach dynamically adjusts the communication rate in response to disagreement in the shared state variable, resulting in a discrete-time-varying, asynchronous network topology. We prove convergence properties of the proposed consensus algorithm, develop metrics to evaluate similar dynamic approaches, and demonstrate the results in simulation, showing our algorithm reduces communication resources, while maintaining fast convergence time.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538613955
StatePublished - Jul 2 2018
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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