@article{f73bb3ad8ada4396b97439aa1277f786,
title = "A Distributed Stochastic Approximation Algorithm for Stochastic LQ Control with Unknown Uncertainty",
abstract = "This paper studies a discrete-time stochastic control problem with linear quadratic criteria over an infinite-time horizon. We focus on control systems whose system matrices are associated with random parameters involving unknown statistical properties. We design a distributed stochastic approximation algorithm to tackle the Riccati equation and derive the optimal controller stabilizing the system. The convergence analysis is provided.",
keywords = "Distributed stochastic approximation, Multiplicative noise, Stochastic control, Unknown statistics",
author = "Zhaorong Zhang and Juanjuan Xu and Xun Li",
note = "Funding Information: This work is supported by the National Natural Science Foundation of China 61821004 , 62250056 , 61922051 , 61873332 and U1806204 , the Natural Science Foundation of Shandong Province, China ZR2021ZD14 and ZR2021JQ24 , Science and Technology Project of Qingdao West Coast New Area, China 2019-32 , 2020-20 and 2020-1-4 , High-level Talent Team Project of Qingdao West Coast New Area, China RCTD-JC-2019-05 , Key Research and Development Program of Shandong Province, China 2020CXGC01208 and the Research Grant Council of Hong Kong, China 15216720 , 15221621 and 15226922 . Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",
year = "2023",
month = may,
doi = "10.1016/j.automatica.2023.110917",
language = "English",
volume = "151",
pages = "1--5",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Ltd",
}