{"title":"Towards Optimal Spatio-Temporal Decomposition of Control-Related Sum-of-Squares Programs","authors":"Vít Cibulka, Milan Korda, Tomáš Haniš","doi":"arxiv-2409.11196","DOIUrl":null,"url":null,"abstract":"This paper presents a method for calculating the Region of Attraction (ROA)\nof nonlinear dynamical systems, both with and without control. The ROA is\ndetermined by solving a hierarchy of semidefinite programs (SDPs) defined on a\nsplitting of the time and state space. Previous works demonstrated that this\nsplitting could significantly enhance approximation accuracy, although the\nimprovement was highly dependent on the ad-hoc selection of split locations. In\nthis work, we eliminate the need for this ad-hoc selection by introducing an\noptimization-based method that performs the splits through conic\ndifferentiation of the underlying semidefinite programming problem. We provide\nthe differentiability conditions for the split ROA problem, prove the absence\nof a duality gap, and demonstrate the effectiveness of our method through\nnumerical examples.","PeriodicalId":501286,"journal":{"name":"arXiv - MATH - Optimization and Control","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - MATH - Optimization and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a method for calculating the Region of Attraction (ROA)
of nonlinear dynamical systems, both with and without control. The ROA is
determined by solving a hierarchy of semidefinite programs (SDPs) defined on a
splitting of the time and state space. Previous works demonstrated that this
splitting could significantly enhance approximation accuracy, although the
improvement was highly dependent on the ad-hoc selection of split locations. In
this work, we eliminate the need for this ad-hoc selection by introducing an
optimization-based method that performs the splits through conic
differentiation of the underlying semidefinite programming problem. We provide
the differentiability conditions for the split ROA problem, prove the absence
of a duality gap, and demonstrate the effectiveness of our method through
numerical examples.