Data-Driven Stabilizing Controller Design for Linear Infinite Networks
Provides a first data-driven control framework for infinite networks, addressing a fundamental problem in large-scale system control.
Proposed a direct data-driven method to design stabilizing controllers for infinite networks of unknown linear subsystems using noisy input-state trajectories, achieving uniform global exponential stability under a compositional small-gain condition.
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.