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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.17597 |
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Table of Contents:
- Distributed controller synthesis offers scalable and privacy-preserving control design, but typical state-of-the-art approaches either assume white-box models or resort to centralized synthesis. In this paper, we combine partially known model knowledge and an input-state dataset within a distributed gray-box scheme to design \(\mathcal{H}_2\) controllers. Our method can handle unknown dynamics and offers scalable synthesis. Each agent communicates with a set of neighbors determined by the physical coupling topology of the system such that we can apply the Alternating Direction Method of Multipliers (ADMM) to solve the problem iteratively in a fully distributed fashion (i.e., without a central server). The effectiveness and flexibility of the proposed approach is demonstrated in simulations of the IEEE 39-bus power system test case.