Learning control for polynomial systems using sum of squares relaxations
Systems and Control
2020-10-05 v2 Systems and Control
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.
Cite
@article{arxiv.2004.00850,
title = {Learning control for polynomial systems using sum of squares relaxations},
author = {Meichen Guo and Claudio De Persis and Pietro Tesi},
journal= {arXiv preprint arXiv:2004.00850},
year = {2020}
}