Distributed Tracking via Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm

Victoria Erofeeva, Oleg Granichin, Natalia Amelina, Yury Ivanskiy, Yuming Jiang

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

1 Цитирования (Scopus)

Аннотация

Networked systems comprised of multiple nodes with sensing, processing, and communication capabilities are able to provide more accurate estimates of some state of a dynamic process through communication between the network nodes. This paper considers the distributed estimation or tracking problem and focuses on a class of consensus normalized algorithms. A distributed algorithm consisting of two well-studied parts, namely, Simultaneous Perturbation Stochastic Approximation (SPSA) and the consensus approach is proposed for networked systems with uncertainties. Such combination allows us to relax the assumption regarding the strong convexity of the minimized mean-risk functional, which may not be fulfilled in the distributed optimization problems. For the proposed algorithm we get a mean squared upper bound of residual between estimates and unknown states. The theoretically established properties of proposed algorithm are illustrated by simulation results.

Язык оригиналаанглийский
Название основной публикации2019 IEEE 58th Conference on Decision and Control, CDC 2019
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы6050-6055
Число страниц6
ISBN (электронное издание)9781728113982
DOI
СостояниеОпубликовано - дек 2019
Событие58th IEEE Conference on Decision and Control, CDC 2019 - Nice, Франция
Продолжительность: 11 дек 201913 дек 2019

Серия публикаций

НазваниеIEEE Conference on Decision and Control
ИздательIEEE
ISSN (печатное издание)0743-1546

конференция

конференция58th IEEE Conference on Decision and Control, CDC 2019
СтранаФранция
ГородNice
Период11/12/1913/12/19

Предметные области Scopus

  • Системотехника
  • Моделирование и симуляция
  • Теория оптимизации

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