Learning control for polynomial systems using sum of squares relaxations

Meichen Guo; Claudio De Persis; Pietro Tesi

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arXiv · 2004.00850

Learning control for polynomial systems using sum of squares relaxations

Meichen Guo·Claudio De Persis·Pietro Tesi

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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.

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Meichen Guo, Claudio De Persis, Pietro Tesi. 2020-04-02. Learning control for polynomial systems using sum of squares relaxations. https://arxiv.org/abs/2004.00850

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