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Learning control for polynomial systems using sum of squares relaxations
arXiv - CS - Systems and Control Pub Date : 2020-04-02 , DOI: arxiv-2004.00850
Meichen Guo, Claudio De Persis, Pietro Tesi

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.

中文翻译:

使用平方和松弛的多项式系统的学习控制

本文考虑了直接从数据中学习非线性多项式系统的控制律的问题,这些数据是在有限时间段内在实验中收集的输入-输出测量值。在没有明确识别系统动力学的情况下,仅使用实验数据直接为非线性多项式系统设计稳定定律。通过使用基于数据的平方和规划,可以构建稳定状态相关的控制增益。
更新日期:2020-10-05
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