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arXiv · 2606.17509

Data-Driven Stabilizing Controller Design for Linear Infinite Networks

Mahdieh Zaker·Andrii Mironchenko·Amy Nejati·Abolfazl Lavaei
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Abstract

We propose a direct data-driven method for controller synthesis of infinite networks composed of unknown linear time-invariant subsystems. Using a single set of noise-corrupted input-state trajectories collected from each subsystem, and provided that certain linear matrix inequalities hold, each subsystem is rendered exponentially input-to-state stable (eISS) by locally constructing an eISS control Lyapunov function together with an exponentially input-to-state stabilizing feedback controller. We then compose these local components under a compositional small-gain condition in infinite-dimensional spaces to obtain a global control Lyapunov function and an associated stabilizing controller, ensuring uniform global exponential stability of the infinite network. The approach is validated on a physical case study with unknown dynamics.

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Mahdieh Zaker, Andrii Mironchenko, Amy Nejati, Abolfazl Lavaei. 2026-06-16. Data-Driven Stabilizing Controller Design for Linear Infinite Networks. https://arxiv.org/abs/2606.17509

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