A data-driven approach builds local exponentially input-to-state stabilizing controllers from noisy data per subsystem and composes them via small-gain conditions to achieve uniform global exponential stability for infinite LTI networks.
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A scenario optimization approach certifies delta-GAS for unknown homogeneous networks by building subsystem delta-ISS Lyapunov functions from noisy data and composing them via small-gain conditions with correctness guarantees.
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Data-Driven Stabilizing Controller Design for Linear Infinite Networks
A data-driven approach builds local exponentially input-to-state stabilizing controllers from noisy data per subsystem and composes them via small-gain conditions to achieve uniform global exponential stability for infinite LTI networks.
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Data-Driven Incremental GAS Certificate of Nonlinear Homogeneous Networks: A Scenario Approach with Noisy Data
A scenario optimization approach certifies delta-GAS for unknown homogeneous networks by building subsystem delta-ISS Lyapunov functions from noisy data and composing them via small-gain conditions with correctness guarantees.