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• W2964791347 abstract "We consider the problem of set invariance verification in black-box nonlinear systems without analytic dynamical models. A data-driven set invariance verification approach relying on the observation of trajectories is proposed to determine almost-invariant sets, which are invariant almost everywhere except possibly in a small subset. With these observations, scenario optimization problems are formulated. We show that probabilistic invariance guarantees on the almost-invariant sets can be established. To get explicit expressions of such sets, a set identification procedure is designed by the use of a polynomial classifier. The practical performance of the proposed data-driven framework is illustrated by numerical examples." @default.
• W2964791347 created "2019-08-13" @default.
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• W2964791347 date "2021-01-01" @default.
• W2964791347 modified "2023-10-09" @default.
• W2964791347 title "Scenario-Based Set Invariance Verification for Black-Box Nonlinear Systems" @default.
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• W2964791347 doi "https://doi.org/10.1109/lcsys.2020.3001882" @default.
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