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arXiv · 2202.09266

Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds

Tom R. V. Steentjes·Mircea Lazar·Paul M. J. Van den Hof
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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 $\mathcal{H}_2$/$\mathcal{H}_\infty$ control through vertex/half-space representations of the polyhedral set of unfalsified systems.

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Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof. 2022-02-18. Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds. https://arxiv.org/abs/2202.09266

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