Decentralized Optimal Control for Linear Stochastic Systems with Control Signals subject to Unknown Noises

Zhaorong Zhang, Juanjuan Xu, Minyue Fu, Xun Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Decentralized strategies have been extensively applied to LQ optimal control problems, whereas, stochastic systems with unknown random parameters have not been comprehensively studied. In this paper, we consider a class of stochastic systems with a decentralized configuration consisting of multiple controllers which have access to Gaussian noises with unknown statistical information. The stabilizing and optimal control strategies are acquired by designing a novel stochastic approximation algorithm recursively evaluating the zero points of certain matrix equations, which is confirmed to be equivalent with solving the corresponding Riccati equations. The proof of convergence and boundness of the proposed algorithm is presented.

Original languageEnglish
Title of host publication2024 IEEE 18th International Conference on Control and Automation, ICCA 2024
PublisherIEEE Computer Society
Pages186-191
Number of pages6
ISBN (Electronic)9798350354409
DOIs
Publication statusPublished - 2024
Event18th IEEE International Conference on Control and Automation, ICCA 2024 - Reykjavik, Iceland
Duration: 18 Jun 202421 Jun 2024

Publication series

NameIEEE International Conference on Control and Automation, ICCA
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference18th IEEE International Conference on Control and Automation, ICCA 2024
Country/TerritoryIceland
CityReykjavik
Period18/06/2421/06/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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