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• W3119473003 abstract "This paper considers the problem of learning control laws for nonlinear polynomial systems directly from the data, which are input-output measurements collected in an experiment over a finite time period. Without explicitly identifying the system dynamics, stabilizing laws are directly designed for nonlinear polynomial systems using experimental data alone. By using data-based sum of square programming, the stabilizing state-dependent control gains can be constructed." @default.
• W3119473003 created "2021-01-18" @default.
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• W3119473003 date "2020-12-14" @default.
• W3119473003 modified "2023-10-16" @default.
• W3119473003 title "Learning control for polynomial systems using sum of squares relaxations" @default.
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• W3119473003 doi "https://doi.org/10.1109/cdc42340.2020.9303924" @default.
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