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dc.contributor.authorQi, Qingyuanen_US
dc.contributor.authorXie, Lihuaen_US
dc.contributor.authorZhang, Huanshuien_US
dc.identifier.citationQi, Q., Xie, L. & Zhang, H. (2020). Optimal control for stochastic systems with multiple controllers of different information structures. IEEE Transactions On Automatic Control, 66(9), 4160-4175.
dc.description.abstractIn this article, we investigate the optimal linear quadratic control problem for stochastic systems with multiple controllers, where each controller has its own information structure, which differs from each other. More specifically, we consider the optimal control problem for systems with multiple controllers of different delayed state information. First, the necessary and sufficient solvability conditions are given in terms of forward and backward difference equations (FBSDEs). Further, an innovation method is proposed to decouple the FBSDEs, and the optimal control strategies are derived based on a given nonsymmetric Riccati equation. Finally, a numerical example is provided to show the effectiveness of the main results. It is stressed that the proposed methods and results can be seen as an important addition to the optimal control theory with asymmetric-information-structure controllers.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.relation.ispartofIEEE Transactions on Automatic Controlen_US
dc.rights© 2020 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOptimal control for stochastic systems with multiple controllers of different information structuresen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsDifferent Information Structuresen_US
dc.subject.keywordsMultiple Controllersen_US
dc.description.acknowledgementThis work was supported in part by the Agency for Science, Technology and Research of Singapore under Grant A1788a0023, in part by the National Natural Science Foundation of China under Grant 61903210, Grant 61633014, and Grant 61873179, in part by the Natural Science Foundation of Shandong Province under Grant ZR2019BF002, in part by the China Postdoctoral Science Foundation under Grant 2019M652324, and in part by the Qingdao Postdoctoral Application Research Project.en_US
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