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arXiv · 2504.08484

Interpolation Conditions for Data Consistency and Prediction in Noisy Linear Systems

Martina Vanelli·Nima Monshizadeh·Julien M. Hendrickx
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Abstract

We develop an interpolation-based framework for noisy linear systems with unknown system matrix with bounded norm (implying bounded growth or non-increasing energy), and bounded process noise energy. The proposed approach characterizes all trajectories consistent with the measured data and these prior bounds in a purely data-driven manner. This characterization enables data-consistency verification, inference, and one-step ahead prediction, which can be leveraged for safety verification and cost minimization. Ultimately, this work represents a preliminary step toward exploiting interpolation conditions in data-driven control, offering a systematic way to characterize trajectories consistent with a dynamical system within a given class and enabling their use in control design.

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Martina Vanelli, Nima Monshizadeh, Julien M. Hendrickx. 2025-04-11. Interpolation Conditions for Data Consistency and Prediction in Noisy Linear Systems. https://arxiv.org/abs/2504.08484

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