Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds
Abstract
Modeling and control of dynamical systems rely on measured data, which contains information about the system. Finite data measurements typically lead to a set of system models that are unfalsified, i.e., that explain the data. The problem of data-informativity for stabilization or control with quadratic performance is concerned with the existence of a controller that stabilizes all unfalsified systems or achieves a desired quadratic performance. Recent results in the literature provide informativity conditions for control based on input-state data and ellipsoidal noise bounds, such as energy or magnitude bounds. In this paper, we consider informativity of input-state data for control where noise bounds are defined through the cross-covariance of the noise with respect to an instrumental variable; bounds that were introduced originally as a noise characterization in parameter bounding identification. The considered cross-covariance bounds are defined by a finite number of hyperplanes, which induce a (possibly unbounded) polyhedral set of unfalsified systems. We provide informativity conditions for input-state data with polyhedral cross-covariance bounds for stabilization and / control through vertex/half-space representations of the polyhedral set of unfalsified systems.
Keywords
Cite
@article{arxiv.2202.09266,
title = {Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds},
author = {Tom R. V. Steentjes and Mircea Lazar and Paul M. J. Van den Hof},
journal= {arXiv preprint arXiv:2202.09266},
year = {2022}
}