Data-driven Linear Quadratic Regulation via Semidefinite Programming
Systems and Control
2020-08-13 v2 Systems and Control
Optimization and Control
Abstract
This paper studies the finite-horizon linear quadratic regulation problem where the dynamics of the system are assumed to be unknown and the state is accessible. Information on the system is given by a finite set of input-state data, where the input injected in the system is persistently exciting of a sufficiently high order. Using data, the optimal control law is then obtained as the solution of a suitable semidefinite program. The effectiveness of the approach is illustrated via numerical examples.
Cite
@article{arxiv.1911.07767,
title = {Data-driven Linear Quadratic Regulation via Semidefinite Programming},
author = {Monica Rotulo and Claudio De Persis and Pietro Tesi},
journal= {arXiv preprint arXiv:1911.07767},
year = {2020}
}
Comments
Accepted for publication in the IFAC World Congress 2020