Online Adversarial Stabilization of Unknown Networked Systems
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Abstract (arXiv)
We investigate the problem of stabilizing an unknown networked linear system under communication constraints and adversarial disturbances. We propose the first provably stabilizing algorithm for the problem. The algorithm uses a distributed version of nested convex body chasing to maintain a consistent estimate of the network dynamics and applies system level synthesis to determine a distributed controller based on this estimated model. Our approach avoids the need for system identification and accommodates a broad class of communication delay while being fully distributed and scaling favorably with the number of subsystems.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Mar 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 3.
5 papers.
- 2023Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysiscited by 6
- 2022Learning Decentralized Linear Quadratic Regulators with √T Regretcited by 1
- 2026Achieving widetildeO(1/ε) Sample Complexity for Bilinear Systems Identification under Bounded Noisescited by 0
- 2026Set Membership Estimation for Stochastic Linear Contextual Banditscited by 0
- 2026Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertaintycited by 0
- 2026Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty13 Aug 2026
- 2026Set Membership Estimation for Stochastic Linear Contextual Bandits18 Jun 2026
- 2026Achieving widetildeO(1/ε) Sample Complexity for Bilinear Systems Identification under Bounded Noises21 Mar 2026
- 2023Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis26 Sep 2023
- 2022Learning Decentralized Linear Quadratic Regulators with √T Regret17 Oct 2022