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• W3091588284 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.
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• W3091588284 date "2020-04-02" @default.
• W3091588284 modified "2023-10-16" @default.
• W3091588284 title "Learning control for polynomial systems using sum of squares relaxations" @default.
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• W3091588284 doi "https://doi.org/10.48550/arxiv.2004.00850" @default.
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