On data-driven control: informativity of noisy input-output data with cross-covariance bounds
Abstract
In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization in terms of cross-covariance bounds and informativity conditions for control based on input-output data. Previous works have derived necessary and sufficient informativity conditions for noisy input-state data with quadratic noise bounds via an S-procedure. Although these bounds do not capture cross-covariance bounds in general, we show that the S-procedure is still applicable for obtaining non-conservative conditions on the data. Informativity-conditions for stability, and control are developed, which are sufficient for input-output data and also necessary for input-state data. Simulation experiments illustrate that cross-covariance bounds can be less conservative for informativity, compared to norm bounds typically employed in the literature.
Cite
@article{arxiv.2111.14193,
title = {On data-driven control: informativity of noisy input-output data with cross-covariance bounds},
author = {Tom R. V. Steentjes and Mircea Lazar and Paul M. J. Van den Hof},
journal= {arXiv preprint arXiv:2111.14193},
year = {2022}
}
Comments
In the previous version of this arXiv article and the corresponding L-CSS article, DOI: 10.1109/LCSYS.2021.3139526, there was a typographical error in equation (21) and (25). The equations contain $P_-$, which should be $Y_-$. This typo is corrected in this version