标题:SOCIAL OPTIMA IN MEAN FIELD LINEAR-QUADRATIC-GAUSSIAN MODELS WITH MARKOV JUMP PARAMETERS
作者:Wang, Bing-Chang; Zhang, Ji-Feng
作者机构:[Wang, Bing-Chang] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.; [Zhang, Ji-Feng] Chinese Acad Sci, Inst Syst Sci, Key Lab 更多
来源:SIAM JOURNAL ON CONTROL AND OPTIMIZATION
出版年:2017
卷:55
期:1
页码:429-456
DOI:10.1137/15M104178X
关键词:mean field model; team decision problem; social optimum; LQG control;; Markov jump parameter
摘要:This paper investigates social optima of mean field linear-quadratic-Gaussian (LQG) control models with Markov jump parameters. The common objective of the agents is to minimize a social cost-the cost average of the whole society. In the cost functions there are coupled mean field terms. First, we consider the centralized case and get a parameterized equation of mean field effect. Then, we design a set of distributed strategies by solving a limiting optimal control problem in an augmented state space subject to the consistency requirement for mean field approximation. It is shown that the set of distributed strategies is asymptotically team-optimal, and the asymptotically optimal social cost value can be obtained explicitly. The optimal social average cost is compared with the optimal individual cost in mean field games by virtue of the explicit expressions, and the difference is further illustrated by a numerical example.
收录类别:EI;SCOPUS;SCIE;SSCI
WOS核心被引频次:1
Scopus被引频次:2
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014341458&doi=10.1137%2f15M104178X&partnerID=40&md5=c45e3bb3851fab0ae5a68c293b2aed0c
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