{"title":"Distributionally robust LQG control under distributed uncertainty","authors":"Lucia Falconi , Augusto Ferrante , Mattia Zorzi","doi":"10.1016/j.automatica.2025.112128","DOIUrl":null,"url":null,"abstract":"<div><div>A new paradigm is proposed for the robustification of the LQG controller against distributional uncertainties on the noise process. Our controller optimizes the closed-loop performance in the worst possible scenario under the constraint that the noise distributional aberrance does not exceed a certain threshold limiting the relative entropy between the actual noise distribution and the nominal one. The main novelty is that the bounds on the distributional aberrance can be arbitrarily distributed along the whole disturbance trajectory. This is a problem for which, notwithstanding significant attention given in the recent literature, so far only relaxed or approximated solutions have been derived. We denote this Distributed uncertainty Distributionally robust LQG problem with the acronym D<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-LQG.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"174 ","pages":"Article 112128"},"PeriodicalIF":4.8000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109825000196","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
A new paradigm is proposed for the robustification of the LQG controller against distributional uncertainties on the noise process. Our controller optimizes the closed-loop performance in the worst possible scenario under the constraint that the noise distributional aberrance does not exceed a certain threshold limiting the relative entropy between the actual noise distribution and the nominal one. The main novelty is that the bounds on the distributional aberrance can be arbitrarily distributed along the whole disturbance trajectory. This is a problem for which, notwithstanding significant attention given in the recent literature, so far only relaxed or approximated solutions have been derived. We denote this Distributed uncertainty Distributionally robust LQG problem with the acronym D-LQG.
期刊介绍:
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