pith. sign in

arxiv: 2605.17597 · v1 · pith:4C3CSBPNnew · submitted 2026-05-17 · 📡 eess.SY · cs.SY

Distributed Synthesis of Gray-Box Distributed H2 Controllers

Michael C. A. Nestor , Fei Teng This is my paper

Pith reviewed 2026-05-19 22:05 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords distributed synthesisgray-box controlH2 controllersADMMdata-driven methodspower systemsscalable control
0
0 comments X

The pith

Partially known dynamics and local data enable fully distributed design of H2 controllers using physical coupling for communication.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a way to synthesize H2 controllers for large interconnected systems when the full dynamics are not known. It mixes the available model knowledge with an input-state dataset inside a gray-box representation to capture the missing parts. The resulting optimization is solved with the alternating direction method of multipliers so that each subsystem communicates only with neighbors defined by the physical couplings. No central coordinator or complete model is required. This setup is relevant for applications such as power grids where collecting every detail centrally is costly or impossible.

Core claim

The authors present a distributed gray-box scheme for H2 controller synthesis that uses known model knowledge together with an input-state dataset to account for unknown dynamics. By defining communication based on the physical coupling topology, the alternating direction method of multipliers is applied to solve the problem iteratively without requiring a central server.

What carries the argument

Distributed optimization of the gray-box H2 cost via the alternating direction method of multipliers, with communication neighbors set by physical couplings.

If this is right

  • Large systems can be controlled optimally without centralized computation or full models.
  • Unknown dynamics are handled through data without complete system identification.
  • The design process preserves privacy by limiting information exchange to neighbors.
  • The approach is demonstrated effective on the IEEE 39-bus power system test case.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be adapted for other control objectives like minimizing worst-case disturbances.
  • Future work might explore using the same structure for adaptive control as data accumulates over time.
  • Connections to decentralized estimation problems could allow joint controller and observer design.

Load-bearing premise

The physical coupling topology is known in advance and directly usable to set up the neighbor communication graph, while the input-state dataset is sufficient to overcome the unknown dynamics in the gray-box model.

What would settle it

A simulation or experiment where the distributed controllers fail to achieve comparable closed-loop H2 performance to a centralized full-model design on the same system would falsify the claim of effective synthesis.

Figures

Figures reproduced from arXiv: 2605.17597 by Fei Teng, Michael C. A. Nestor.

Figure 1
Figure 1. Figure 1: The neighborhood dynamics (1a) for subsystem [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Convergence of control performance as the number [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Squared closed-loop H2-norm under control design methods (i)-(v) achieves indistinguishable performance compared to the H2 benchmark (v), whilst our distributed black-box (i) and gray-box (ii) schemes have around 10% performance degradation due to the conservatism introduced to decou￾ple the global Lyapunov matrix and constraint (7c) and the restriction of controller structure by GC . Centralized black-box… view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a distributed gray-box approach for synthesizing H2 controllers by combining partial known model dynamics with an input-state dataset. Communication occurs only with neighbors defined by the known physical coupling topology, allowing the Alternating Direction Method of Multipliers (ADMM) to solve the synthesis problem iteratively in a fully distributed manner without a central server. The method is demonstrated via simulations on the IEEE 39-bus power system test case.

Significance. If the gray-box representation can be shown to produce controllers with reliable H2 performance, the work would offer a practical route to scalable, privacy-preserving controller design for large interconnected systems where full models are unavailable. The explicit use of ADMM on a topology-derived graph and the choice of a standard power-system benchmark are concrete strengths that support potential impact in applications such as grid control.

major comments (2)
  1. [Abstract and gray-box modeling section] Abstract and gray-box modeling section: the claim that a finite input-state dataset compensates for unknown dynamics to recover near-optimal H2 controllers is load-bearing, yet no persistency-of-excitation conditions, identifiability requirements, or a priori error bounds on the residual dynamics are supplied. Without these, it is unclear whether the subsequent ADMM decomposition can guarantee the stated performance.
  2. [Distributed synthesis via ADMM] Distributed synthesis via ADMM: the neighbor graph is fixed by the physical coupling topology, so any modeling error remaining after gray-box compensation cannot be mitigated by extra communication. No analysis is given of how such residual error propagates through the ADMM iterations or affects the achieved H2 norm.
minor comments (2)
  1. The abstract states that the approach offers 'flexibility' but does not indicate which parameters (dataset length, topology density, or uncertainty level) can be varied while preserving the guarantees.
  2. [Simulation results] Simulation results would benefit from explicit reporting of the achieved H2 norms, comparison against fully centralized or purely data-driven baselines, and a brief sensitivity study with respect to dataset size.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the positive assessment of the work's potential impact. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract and gray-box modeling section] Abstract and gray-box modeling section: the claim that a finite input-state dataset compensates for unknown dynamics to recover near-optimal H2 controllers is load-bearing, yet no persistency-of-excitation conditions, identifiability requirements, or a priori error bounds on the residual dynamics are supplied. Without these, it is unclear whether the subsequent ADMM decomposition can guarantee the stated performance.

    Authors: We agree that the manuscript would benefit from explicit discussion of data assumptions. The gray-box formulation combines the known partial dynamics with the input-state dataset to approximate the residual dynamics, and the IEEE 39-bus simulations show that the resulting controllers achieve good H2 performance in practice. In the revised manuscript we will add a subsection in the gray-box modeling section that states the working assumption of sufficient data richness (without claiming full persistency-of-excitation guarantees) and derives preliminary a priori bounds on the residual approximation error in terms of dataset length and the known model structure. A complete identifiability analysis is noted as future work. revision: yes

  2. Referee: [Distributed synthesis via ADMM] Distributed synthesis via ADMM: the neighbor graph is fixed by the physical coupling topology, so any modeling error remaining after gray-box compensation cannot be mitigated by extra communication. No analysis is given of how such residual error propagates through the ADMM iterations or affects the achieved H2 norm.

    Authors: We concur that propagation of residual modeling error through the fixed-topology ADMM iterations merits analysis. The current development assumes the gray-box approximation is adequate for the target application, as supported by the numerical results. In the revision we will insert a new subsection that provides a first-order perturbation analysis of how bounded residual errors affect ADMM convergence and the closed-loop H2 norm, leveraging the structure of the augmented Lagrangian and the topology-induced sparsity. revision: yes

Circularity Check

0 steps flagged

No circularity: method combines partial models, datasets, and ADMM on known topology without self-referential reduction.

full rationale

The paper presents a gray-box H2 synthesis approach that fuses a partially known model with an input-state dataset, then applies ADMM for distributed solution where the communication graph is taken directly from the known physical coupling topology. No derivation step is shown to reduce by construction to a fitted parameter or to a self-citation chain; the central claims rest on the external assumptions that the dataset is sufficiently rich and the topology is known, which are stated rather than derived from the method itself. The approach is described as a scalable combination of existing techniques (gray-box modeling, ADMM) without renaming known results or smuggling ansatzes via self-citation. This is a standard, self-contained synthesis of prior methods with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no specific free parameters, axioms, or invented entities can be extracted or evaluated.

pith-pipeline@v0.9.0 · 5649 in / 1110 out tokens · 42044 ms · 2026-05-19T22:05:42.134960+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

79 extracted references · 79 canonical work pages

  1. [1]

    Optimized distributed control and network topology design for interconnected systems , year=

    Groß, Dominic and Stursberg, Olaf , booktitle=. Optimized distributed control and network topology design for interconnected systems , year=

    work page

  2. [2]

    Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness , year=

    De Persis, Claudio and Tesi, Pietro , journal=. Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness , year=

    work page

  3. [3]

    1999 , note =

    SPC: Subspace Predictive Control , journal =. 1999 , note =. doi:https://doi.org/10.1016/S1474-6670(17)56683-5 , author =

    work page doi:10.1016/s1474-6670(17)56683-5 1999

  4. [4]

    Müller and Frank Allgöwer , abstract =

    Julian Berberich and Johannes Köhler and Matthias A. Müller and Frank Allgöwer , abstract =. On the design of terminal ingredients for data-driven MPC , journal =. 2021 , note =. doi:https://doi.org/10.1016/j.ifacol.2021.08.554 , url =

    work page doi:10.1016/j.ifacol.2021.08.554 2021

  5. [5]

    On the Design of Structured Stabilizers for LTI Systems , year=

    Ferrante, Francesco and Dabbene, Fabrizio and Ravazzi, Chiara , journal=. On the Design of Structured Stabilizers for LTI Systems , year=

    work page

  6. [6]

    , journal=

    Shih-Ho Wang and Davison, E. , journal=. On the stabilization of decentralized control systems , year=

    work page

  7. [7]

    Pichai and M.E

    V. Pichai and M.E. Sezer and D.D. Šiljak , keywords =. A graph-theoretic characterization of structurally fixed modes , journal =. 1984 , issn =. doi:https://doi.org/10.1016/0005-1098(84)90033-5 , url =

    work page doi:10.1016/0005-1098(84)90033-5 1984

  8. [8]

    2011 , publisher=

    Linear Matrix Inequalities in Control , author=. 2011 , publisher=

    work page 2011

  9. [9]

    Jovanović and Neil K

    Mihailo R. Jovanović and Neil K. Dhingra , keywords =. Controller architectures: Tradeoffs between performance and structure , journal =. 2016 , note =. doi:https://doi.org/10.1016/j.ejcon.2016.05.003 , url =

    work page doi:10.1016/j.ejcon.2016.05.003 2016

  10. [10]

    2024 , month =

    Nestor, Michael and Teng, Fei , title =. 2024 , month =

    work page 2024

  11. [11]

    Jones and Manfred Morari and Melanie N

    Christian Conte and Colin N. Jones and Manfred Morari and Melanie N. Zeilinger , keywords =. Distributed synthesis and stability of cooperative distributed model predictive control for linear systems , journal =. 2016 , issn =. doi:https://doi.org/10.1016/j.automatica.2016.02.009 , abstract =

    work page doi:10.1016/j.automatica.2016.02.009 2016

  12. [12]

    Data-Driven Distributed and Localized Model Predictive Control , year=

    Alonso, Carmen Amo and Yang, Fengjun and Matni, Nikolai , journal=. Data-Driven Distributed and Localized Model Predictive Control , year=

    work page

  13. [13]

    Data-Enabled Predictive Control: In the Shallows of the DeePC , doi =

    Coulson, Jeremy and Lygeros, John and Dörfler, Florian , booktitle =. Data-Enabled Predictive Control: In the Shallows of the DeePC , doi =. 2019 , month =

    work page 2019

  14. [14]

    Willems and Paolo Rapisarda and Ivan Markovsky and Bart L.M

    Jan C. Willems and Paolo Rapisarda and Ivan Markovsky and Bart L.M. A note on persistency of excitation , journal =. 2005 , issn =. doi:https://doi.org/10.1016/j.sysconle.2004.09.003 , url =

    work page doi:10.1016/j.sysconle.2004.09.003 2005

  15. [15]

    and Allgöwer, Frank , journal=

    Berberich, Julian and Scherer, Carsten W. and Allgöwer, Frank , journal=. Combining Prior Knowledge and Data for Robust Controller Design , year=

    work page

  16. [16]

    An Overview of Systems-Theoretic Guarantees in Data-Driven Model Predictive Control

    Berberich, Julian and Allgöwer, Frank. An Overview of Systems-Theoretic Guarantees in Data-Driven Model Predictive Control. Annu. Rev. Control, Robotics, and Autonomous Syst. 2024. doi:https://doi.org/10.1146/annurev-control-030323-024328

    work page doi:10.1146/annurev-control-030323-024328 2024

  17. [17]

    Identifiability in the Behavioral Setting , year=

    Markovsky, Ivan and Dörfler, Florian , journal=. Identifiability in the Behavioral Setting , year=

    work page

  18. [18]

    2024 , note=

    Beyond the fundamental lemma: from finite time series to linear system , author=. 2024 , note=

    work page 2024

  19. [19]

    Müller and Frank Allgöwer , keywords =

    Matthias Köhler and Julian Berberich and Matthias A. Müller and Frank Allgöwer , keywords =. Data-driven distributed MPC of dynamically coupled linear systems , journal =. 2022 , note =. doi:https://doi.org/10.1016/j.ifacol.2022.11.080 , abstract =

    work page doi:10.1016/j.ifacol.2022.11.080 2022

  20. [20]

    Structured low-rank approximation and its applications , journal =

    Ivan Markovsky , keywords =. Structured low-rank approximation and its applications , journal =. 2008 , issn =. doi:https://doi.org/10.1016/j.automatica.2007.09.011 , url =

    work page doi:10.1016/j.automatica.2007.09.011 2008

  21. [21]

    Behavioral theory for stochastic systems? A data-driven journey from Willems to Wiener and back again , journal =

    Timm Faulwasser and Ruchuan Ou and Guanru Pan and Philipp Schmitz and Karl Worthmann , keywords =. Behavioral theory for stochastic systems? A data-driven journey from Willems to Wiener and back again , journal =. 2023 , issn =. doi:https://doi.org/10.1016/j.arcontrol.2023.03.005 , url =

    work page doi:10.1016/j.arcontrol.2023.03.005 2023

  22. [22]

    Distributed Solution of MIQP Problems Arising for Networked Systems with Coupling Constraints , year=

    Liu, Zonglin and Stursberg, Olaf , booktitle=. Distributed Solution of MIQP Problems Arising for Networked Systems with Coupling Constraints , year=

    work page

  23. [23]

    Distributed Solution of Mixed-Integer Programs by ADMM with Closed Duality Gap , year=

    Liu, Zonglin and Stursberg, Olaf , booktitle=. Distributed Solution of Mixed-Integer Programs by ADMM with Closed Duality Gap , year=

    work page

  24. [24]

    Stephen Boyd and Qinping Yang , title =. Int. J. of Control , volume =. 1989 , publisher =. doi:10.1080/00207178908559769 , URL =

    work page doi:10.1080/00207178908559769 1989

  25. [25]

    Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments , journal =

    Linbin Huang and Jianzhe Zhen and John Lygeros and Florian Dörfler , keywords =. Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments , journal =. 2021 , note =. doi:https://doi.org/10.1016/j.ifacol.2021.08.357 , url =

    work page doi:10.1016/j.ifacol.2021.08.357 2021

  26. [26]

    Proceedings of the CACSD Conf

    L. Proceedings of the CACSD Conf. , title =

    work page

  27. [27]

    Version 10.1

    The MOSEK optimization toolbox for MATLAB manual. Version 10.1

    work page

  28. [28]

    Convex Optimization , publisher=

    Boyd, Stephen and Vandenberghe, Lieven , year=. Convex Optimization , publisher=

    work page

  29. [29]

    Provably Robust Verification of Dissipativity Properties from Data , year=

    Koch, Anne and Berberich, Julian and Allgöwer, Frank , journal=. Provably Robust Verification of Dissipativity Properties from Data , year=

    work page

  30. [30]

    2018 , note =

    Decomposing complex plants for distributed control: Perspectives from network theory , journal =. 2018 , note =. doi:https://doi.org/10.1016/j.compchemeng.2017.10.015 , author =

    work page doi:10.1016/j.compchemeng.2017.10.015 2018

  31. [31]

    and Chertkov, Michael and Bullo, Francesco , journal=

    Dörfler, Florian and Jovanović, Mihailo R. and Chertkov, Michael and Bullo, Francesco , journal=. Sparsity-Promoting Optimal Wide-Area Control of Power Networks , year=

    work page

  32. [32]

    2024 , month =

    Miller, Jared and Eising, Jaap and Dörfler, Florian and Smith, Roy , title =. 2024 , month =

    work page 2024

  33. [33]

    , booktitle=

    Pequito, Sérgio and Kar, Soummya and Pappas, George J. , booktitle=. Minimum cost constrained input-output and control configuration co-design problem: A structural systems approach , year=

    work page

  34. [34]

    Topology identification of heterogeneous networks: Identifiability and reconstruction , journal =

    Henk J. Topology identification of heterogeneous networks: Identifiability and reconstruction , journal =. 2021 , issn =. doi:https://doi.org/10.1016/j.automatica.2020.109331 , url =

    work page doi:10.1016/j.automatica.2020.109331 2021

  35. [35]

    Stabilizing Control Structures: An Optimization Framework , year=

    Mosalli, Hesamoddin and Babazadeh, Maryam , journal=. Stabilizing Control Structures: An Optimization Framework , year=

    work page

  36. [36]

    Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection , journal =

    Wentao Tang and Andrew Allman and Davood Babaei Pourkargar and Prodromos Daoutidis , keywords =. Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection , journal =. 2018 , issn =. doi:https://doi.org/10.1016/j.compchemeng.2017.12.010 , url =

    work page doi:10.1016/j.compchemeng.2017.12.010 2018

  37. [37]

    2021 , issn =

    A survey on clustering methods for distributed and networked control systems , journal =. 2021 , issn =. doi:https://doi.org/10.1016/j.arcontrol.2021.08.002 , author =

    work page doi:10.1016/j.arcontrol.2021.08.002 2021

  38. [38]

    Communication Topology Optimization for Time-Delay Cyber–Physical Microgrids Under Distributed Control , year=

    Xu, Luo and Guo, Qinglai and Zeng, Hongtai and Yang, Yue and Feng, Kairui and Sun, Hongbin , journal=. Communication Topology Optimization for Time-Delay Cyber–Physical Microgrids Under Distributed Control , year=

    work page

  39. [39]

    and Kiani, A

    Mozafari, Y. and Kiani, A. and Hirche, S. , booktitle=. Oscillator network synchronization by distributed control , year=

    work page

  40. [40]

    2023 , month=

    Data-driven design of complex network structures to promote synchronization , author=. 2023 , month=. 2309.10941 , archivePrefix=

    work page arXiv 2023

  41. [41]

    , journal=

    Dunbar, William B. , journal=. Distributed Receding Horizon Control of Dynamically Coupled Nonlinear Systems , year=

    work page

  42. [42]

    and Eichler, Annika and Smith, Roy S

    Stürz, Yvonne R. and Eichler, Annika and Smith, Roy S. , journal=. Distributed Control Design for Heterogeneous Interconnected Systems , year=

    work page

  43. [43]

    2015 , School =

    Groß, Dominic , TITLE =. 2015 , School =

    work page 2015

  44. [44]

    and Muñoz de la Peña, D

    Maestre, J.M. and Muñoz de la Peña, D. and Jiménez Losada, A. and Algaba, E. and Camacho, E. F. , title =. Optimal Control Applications and Methods , volume =. doi:https://doi.org/10.1002/oca.2090 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/oca.2090 , abstract =

    work page doi:10.1002/oca.2090 2090

  45. [45]

    Doyle and Steven H

    James Anderson and John C. Doyle and Steven H. Low and Nikolai Matni , abstract =. System level synthesis , journal =. 2019 , issn =. doi:https://doi.org/10.1016/j.arcontrol.2019.03.006 , url =

    work page doi:10.1016/j.arcontrol.2019.03.006 2019

  46. [46]

    and Hug, Gabriela and Dörfler, Florian , journal=

    Fisher, Michael W. and Hug, Gabriela and Dörfler, Florian , journal=. Approximation by Simple Poles—Part I: Density and Geometric Convergence Rate in Hardy Space , year=

    work page

  47. [47]

    and Hug, Gabriela and Dörfler, Florian , journal=

    Fisher, Michael W. and Hug, Gabriela and Dörfler, Florian , journal=. Approximation by Simple Poles—Part II: System Level Synthesis Beyond Finite Impulse Response , year=

    work page

  48. [48]

    , journal=

    Wang, Yuh-Shyang and Matni, Nikolai and Doyle, John C. , journal=. Separable and Localized System-Level Synthesis for Large-Scale Systems , year=

    work page

  49. [49]

    , journal=

    Wang, Yuh-Shyang and Matni, Nikolai and Doyle, John C. , journal=. A System-Level Approach to Controller Synthesis , year=

    work page

  50. [50]

    2024 , note =

    Convex Reformulation of LMI-Based Distributed Controller Design with a Class of Non-Block-Diagonal Lyapunov Functions , author=. 2024 , note =. 2404.04576 , archivePrefix=

    work page arXiv 2024

  51. [51]

    and Lall, S

    Rotkowitz, M. and Lall, S. , journal=. A Characterization of Convex Problems in Decentralized Control , year=

    work page

  52. [52]

    , booktitle=

    Rotkowitz, Michael C. , booktitle=. Parametrization of stabilizing controllers subject to subspace constraints , year=

    work page

  53. [53]

    Stabilizing decentralized systems with arbitrary information structure , year=

    Alavian, Alborz and Rotkowitz, Michael , booktitle=. Stabilizing decentralized systems with arbitrary information structure , year=

    work page

  54. [54]

    An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis , year=

    Furieri, Luca and Zheng, Yang and Papachristodoulou, Antonis and Kamgarpour, Maryam , journal=. An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis , year=

    work page

  55. [55]

    , journal=

    Moothedath, Shana and Chaporkar, Prasanna and Belur, Madhu N. , journal=. Minimum Cost Feedback Selection for Arbitrary Pole Placement in Structured Systems , year=

    work page

  56. [56]

    and Eichler, Annika and Smith, Roy S

    Stürz, Yvonne R. and Eichler, Annika and Smith, Roy S. , journal=. Fixed Mode Elimination by Minimum Communication Within an Estimator-Based Framework for Distributed Control , year=

    work page

  57. [57]

    , journal=

    Lin, Fu and Fardad, Makan and Jovanović, Mihailo R. , journal=. Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers , year=

    work page

  58. [58]

    Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , volume =

    Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato, Borja and Eckstein, Jonathan , year =. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , volume =. Foundations and Trends in Machine Learning , doi =

    work page

  59. [59]

    and Valcher, Maria Elena and Smith, Roy S

    De Pasquale, Giulia and Stürz, Yvonne R. and Valcher, Maria Elena and Smith, Roy S. , booktitle=. Extended Full Block S-Procedure for Distributed Control of Interconnected Systems , year=

    work page

  60. [60]

    Convex Structured Controller Design in Finite Horizon , year=

    Dvijotham, Krishnamurthy and Todorov, Emanuel and Fazel, Maryam , journal=. Convex Structured Controller Design in Finite Horizon , year=

    work page

  61. [61]

    Data-Driven Decentralized Control Design for Discrete-Time Large-Scale Systems , journal =

    Jiaping Liao and Shuaizheng Lu and Tao Wang and Weiming Xiang , keywords =. Data-Driven Decentralized Control Design for Discrete-Time Large-Scale Systems , journal =. 2024 , note =. doi:https://doi.org/10.1016/j.ifacol.2025.01.137 , url =

    work page doi:10.1016/j.ifacol.2025.01.137 2024

  62. [62]

    , booktitle=

    Lian, Yingzhao and Jones, Colin N. , booktitle=. From System Level Synthesis to Robust Closed-loop Data-Enabled Predictive Control , year=

    work page

  63. [63]

    Data-Driven System Level Synthesis , author =. Proc. of the 3rd Conf. on Learning for Dynamics and Control , pages =. 2021 , editor =

    work page 2021

  64. [64]

    Data-driven methods for distributed control of interconnected linear systems

    Steentjes, Tom Robert Vince. Data-driven methods for distributed control of interconnected linear systems. 2022

    work page 2022

  65. [65]

    and Lazar, Mircea and Van den Hof, Paul M.J

    Steentjes, Tom R.V. and Lazar, Mircea and Van den Hof, Paul M.J. , booktitle=. H∞ performance analysis and distributed controller synthesis for interconnected linear systems from noisy input-state data , year=

    work page

  66. [66]

    Controller and Network Design Exploiting System Structure

    Schuler, Simone. Controller and Network Design Exploiting System Structure. 2015

    work page 2015

  67. [67]

    and Calderbank, A

    Chiang, Mung and Low, Steven H. and Calderbank, A. Robert and Doyle, John C. , journal=. Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures , year=

    work page

  68. [68]

    M. C. De Oliveira and J. C. Geromel and J. Bernussou and , title =. Int. J. Control , volume =. 2002 , publisher =. doi:10.1080/00207170210140212 , URL =

    work page doi:10.1080/00207170210140212 2002

  69. [69]

    Controller synthesis from noisy-input noisy-output data,

    Controller Synthesis from Noisy-Input Noisy-Output Data , author=. 2024 , note=. 2402.02588 , archivePrefix=

    work page arXiv 2024

  70. [70]

    and Müller, Matthias A

    Alsalti, Mohammad and Lopez, Victor G. and Müller, Matthias A. , journal=. Notes on Data-driven Output-feedback Control of Linear MIMO Systems , year=

    work page

  71. [71]

    Toward a Systems Theory of Algorithms , year=

    Dörfler, Florian and He, Zhiyu and Belgioioso, Giuseppe and Bolognani, Saverio and Lygeros, John and Muehlebach, Michael , journal=. Toward a Systems Theory of Algorithms , year=

    work page

  72. [72]

    Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems

    Nestor, Michael and Teng, Fei. Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems. Systems Theory in Data and Optimization. 2025

    work page 2025

  73. [73]

    and Poor, H

    Xu, Songcen and de Lamare, Rodrigo C. and Poor, H. Vincent , journal=. Distributed Compressed Estimation Based on Compressive Sensing , year=

    work page

  74. [74]

    and Lazar, M

    Jokic, A. and Lazar, M. , booktitle=. On decentralized stabilization of discrete-time nonlinear systems , year=

    work page

  75. [75]

    Scherer , volume =

    C.W. Scherer , volume =. LPV control and full block multipliers , journal =. 2001 , issn =. doi:https://doi.org/10.1016/S0005-1098(00)00176-X , url =

    work page doi:10.1016/s0005-1098(00)00176-x 2001

  76. [76]

    and Zeilinger, Melanie and Morari, M

    Conte, Christian and Voellmy, N. and Zeilinger, Melanie and Morari, M. and Jones, Colin , year =. Distributed Synthesis and Control of Constrained Linear Systems , booktitle =

    work page

  77. [77]

    and Yin, Wotao , year=

    Ryu, Ernest K. and Yin, Wotao , year=. Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators , publisher=

    work page

  78. [78]

    2025 , note =

    Zhaohua Yang and Yuxing Zhong and Nachuan Yang and Xiaoxu Lyu and Ling Shi , title=. 2025 , note =. 2503.14949 , archivePrefix=

    work page arXiv 2025

  79. [79]

    , booktitle=

    Miller, Jared and Eising, Jaap and Dörfler, Florian and Smith, Roy S. , booktitle=. Data-Driven Structured Robust Control of Linear Systems , year=

    work page