{"title":"Decentralized control for optimal LQ problems in stochastic systems with unknown uncertainties","authors":"Zhaorong Zhang , Juanjuan Xu , Minyue Fu , Xun Li","doi":"10.1016/j.jfranklin.2024.107274","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, we study the optimal control problem of a linear quadratic stochastic system where the randomness results from multiplicative noises. Especially, two controllers having access to different information are involved in the system. Different from most of the existing results which are based on the condition that the information of multiplicative noise is known during the design of optimal controllers, we focus on a more general case that the statistical information of the multiplicative noise is inaccessible. Under this setting, we propose a stochastic approximation algorithm to derive the solutions to algebraic Riccati equations (AREs) and obtain the optimal and stabilizing decentralized controllers.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"361 18","pages":"Article 107274"},"PeriodicalIF":3.7000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224006951","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we study the optimal control problem of a linear quadratic stochastic system where the randomness results from multiplicative noises. Especially, two controllers having access to different information are involved in the system. Different from most of the existing results which are based on the condition that the information of multiplicative noise is known during the design of optimal controllers, we focus on a more general case that the statistical information of the multiplicative noise is inaccessible. Under this setting, we propose a stochastic approximation algorithm to derive the solutions to algebraic Riccati equations (AREs) and obtain the optimal and stabilizing decentralized controllers.
期刊介绍:
The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.