Decentralized parameter estimation by consensus based stochastic approximation

Srdjan S. Stanković, Miloš S. Stanković, Dusan M Stipanovic

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

Abstract

In this paper an algorithm for decentralized estimation of parameters in linear discrete-time regression models is proposed in the form of a combination of local stochastic approximation algorithms and a global consensus strategy. A rigorous analysis of the asymptotic properties of the proposed algorithm is presented, taking into account both the multi-agent network structure and the probabilities of local measurements and communication faults. In the case of non-vanishing gains in the stochastic approximation algorithms, an upper bound of the mean-square estimation error matrix is defined as a solution of a Lyapunov-like matrix equation, while in the case of asymptotically vanishing gains the mean-square convergence is proved. It is also demonstrated how the consensus strategy can contribute to the reduction of measurement noise influence.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages1535-1540
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

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Keywords

  • Consensus strategy
  • Convergence analysis
  • Decentralized estimation
  • Denoising
  • Multi-agent systems
  • Stochastic approximation

ASJC Scopus subject areas

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

Cite this

Stanković, S. S., Stanković, M. S., & Stipanovic, D. M. (2007). Decentralized parameter estimation by consensus based stochastic approximation. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 1535-1540). [4434812] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434812