Data-Driven Communication Topology and Distributed Controller Synthesis: Control-Aware and Co-Design

arXiv:2609.38522 · eess.SY, cs.SY · Submitted 2026-09-29 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: I'm Rosa, and with me are Dev and Taro, guest researcher.

Dev: Today's paper: "Data-Driven Communication Topology and Distributed Controller Synthesis".

Rosa: In distributed control schemes, designing an optimal communication topology that guarantees controller existence while balancing communication costs and control performance is crucial for practical implementation.

Dev: First, who's behind it and why it matters.

Paper summary: Rosa: So we're talking about the paper "Data-Driven Communication Topology and Distributed Controller Synthesis: Control-Aware and Co-Design," which really tackles the challenge of designing communication structures for distributed control when you need to guarantee a controller exists.

Dev: Exactly, Rosa, it seems like this paper moves beyond just picking a network structure; it’s about tying the network design directly into the controller synthesis process itself.

Taro: I'm intrigued by how they handle the trade-off between communication costs and actual control performance in this context. We need to know if these methods translate well when we throw them into a messy, real-world scenario where things aren't perfectly modeled.

Rosa: Well, the core thesis of the paper is that previous data-driven works often treated the topology and controller design as separate steps, which isn't always helpful because you end up with a stabilizing controller that just doesn't perform well under real operating conditions. This paper proposes two main ways to link them: a co-design scheme and a sequential control-aware approach, both aiming to synthesize the structure and the controller simultaneously from data.

Dev: That sounds like it addresses a real issue for us in the control loop; we care deeply about latency and how quickly we can react to disturbances. The co-design scheme, for instance, tries to minimize a combined cost function that balances the communication link costs against the closed-loop control cost.

Taro: If they are co-optimizing both things at once, I wonder what happens when we introduce uncertainty or misbehavior in the environment; does this integrated approach offer more robustness than designing them separately?

Rosa: The co-design formulation is pretty specific: they formulate it as a mixed-integer semidefinite program, or MISDP, which lets you minimize that combined cost function subject to stability, structural, and performance constraints. These constraints ensure the resulting system remains stable and meets certain structural requirements related to the topology.

Dev: I see how that translates into something manageable, although solving an MISDP is always computationally heavy, which raises my immediate concern about real-time loop rates and failure modes. The sequential control-aware scheme seems like a way to tackle that by using relaxations from controller design within the topology selection process.

Taro: Could you elaborate on what the control-aware approach actually prioritizes when it’s selecting links? Does it focus more on ensuring a structure is feasible for *some* controller, or does it try to steer the topology towards one that supports a good controller without explicitly optimizing the controller cost right away?

Rosa: The control-aware scheme has its own cost function where they are minimizing something like the sum of communication link costs minus coupling estimates, defined as eta ij:= ij squared ii squared. This formulation suggests a desire to prioritize communication between subsystems that are already strongly coupled in the system dynamics.

Paper summary: Dev: That coupling strength estimate sounds interesting from a loop rate perspective; if they can use that as a proxy, it might help keep the resulting structure closer to what's needed for fast, reliable control responses. But I still have to ask how stable the system is guaranteed under those link cost trade-offs, given we're dealing with stochastic disturbances epsilon i(k).

Taro: If the system is operating in a dynamic environment where parameters might be unknown, how does this data-driven method handle the uncertainty inherent in those unknown matrices B i, C i, and D i ? Does the method assume some level of structure preservation even when that information is missing?

Rosa: The paper sets up assumptions about the subsystem interconnections being described by graph G P, and assuming that the matrices are observable and controllable, which is a standard starting point. They also enforce structural constraints like delta ij = zero so E u iL E xi,j = zero to ensure the controller structure respects the chosen topology.

Dev: Those structural conditions are what give us some confidence that we aren't just designing a network arbitrarily; it ties the resulting control structure back to the communication layout, which is something I need for predictability in failure analysis. However, they do have to satisfy a stability constraint Cstab based on extended state dynamics derived from an LFT representation.

Taro: It sounds like the authors are focusing heavily on finding *some* stabilizing structure first, and then trying to fit the controller onto it, which is a modular sequential approach they mention as control-agnostic. What happens if that initial structure doesn't allow for the desired control method we are aiming for?

Rosa: Exactly; the authors acknowledge that this initial structure might not support the specific controller method we want to use in closed-loop, which is a limitation they point out. But in terms of results, simulations on systems like the IEEE fourteen-bus power system show that their co-design approach achieves lower synthesis bounds and realized closed-loop costs compared to the control-aware design.

Dev: Lower synthesis bounds are good for us because they suggest a more efficient way to synthesize the controller given our communication constraints. But I have to press on the practical application; Rosa, how long can we realistically expect this data-driven topology design to stay in sync with a dynamic physical system before we need a full redesign?

Taro: If this works outside of the lab, I'd be interested in seeing how it handles unexpected events—say, if one link fails or communication latency spikes drastically during an actual disturbance. Can the system adapt its topology decision quickly enough to maintain performance when things go sideways?

Rosa: That's a huge practical question, Taro; they demonstrate effectiveness on the IEEE fourteen-bus power system simulations, showing it outperforms the control-aware scheme in computation time and performance metrics across varying noise bounds. The results suggest a good balance is struck when using this co-design MISDP.

Paper summary: Dev: While the simulation results are encouraging, I still see the need for further testing on systems with much higher dynamic ranges or more complex failure modes than the power system model we used. The paper does have to be careful about its assumptions; they mention a limitation where performance under control-aware topologies can degrade non-monotonically as the number of links increases due to conservatism introduced by structural constraints.

Taro: Conservatism is something we need to watch out for when deploying anything into critical infrastructure; I'm hoping future work can relax those limitations so the topology design isn't overly cautious in a live setting.

Rosa: So, to wrap up this discussion on "Data-Driven Communication Topology and Distributed Controller Synthesis: Control-Aware and Co-Design," we see two main data-driven ways to link topology and controller design, one being the co-design scheme that directly minimizes the combined cost, and the other being a sequential control-aware approach that uses structural relaxations.

Dev: The implication for us as control engineers is that these methods offer a way to synthesize the necessary communication architecture alongside the controller, which is much more integrated than previous methods. It's about finding a workable synthesis path under data-driven constraints.

Taro: From an autonomy perspective, if we can reliably determine a communication structure that supports a controller even with unknown system parameters, it opens the door for truly decentralized decision-making in complex, unpredictable environments.

Rosa: The overall impact seems to be providing a framework where we can get a better handle on the communication overhead versus how well the system actually performs, which is vital for designing resilient distributed systems.

Dev: Ultimately, this paper gives us a toolset—the co-design MISDP and the control-aware sequential scheme—to move beyond purely topology-based designs toward a holistic synthesis method.

Taro: I think the main takeaway is that while the current method has limitations, especially around real-world applicability regarding link count and parameter uncertainty, it provides a strong foundation for future work exploring topology design within a distributed synthesis setting.

Rosa: That's right; we have seen how these data-driven approaches attempt to balance communication needs with control quality using rigorous mathematical programming tools like the MISDP.

Dev: And while the simulation results on the power system are positive, we keep an eye on those limitations regarding non-monotonic performance degradation as link count increases.

Taro: So, we've looked at the paper's claims about co-optimization and sequential coupling, and it seems like the main challenge moving forward is addressing those real-world constraints on deployment.

Rosa: That’s our overview of how "Data-Driven Communication Topology and Distributed Controller Synthesis: Control-Aware and Co-Design" addresses the need for integrated network and controller design in distributed control schemes.

Conclusion: Rosa: So we've been digging into how this paper tackles designing communication networks and synthesizing controllers all at once using data from real systems, and now we get to wrap up with the conclusion and what this actually means for us.

Dev: I think it’s a really neat piece because they managed to use mixed-integer semidefinite programs to optimize both the network structure and the control gains simultaneously, which is something we've always wanted to see more of in practice.

Taro: I agree, Dev, it feels like they're finally bridging that gap between theoretical control design and practical system architecture where you can handle uncertainty better than traditional methods.

Rosa: The authors really focused on presenting two distinct ways to approach this—a co-design scheme and a control-aware sequential method—and both aim to find that sweet spot between needing good performance and keeping the communication links cheap.

Dev: That balance is exactly what keeps me interested; we always struggle with finding that trade-off where a faster loop rate doesn't require an impossibly complex or expensive network structure.

Taro: And if you look at the implications, this approach could mean we can design decentralized control systems for things like autonomous vehicles or smart grids where the communication infrastructure is still being built as part of the control problem itself.

Rosa: It definitely opens up possibilities for creating more self-aware and adaptable distributed systems, which is exciting because it moves us beyond rigid, pre-defined topologies.

Dev: I'm curious about how they handle those real-world deployment scenarios we talked about earlier—specifically, how long this kind of data-driven synthesis remains valid when the physical system itself starts to drift or fail over time.

Taro: That’s a crucial point; if the underlying dynamics change significantly, we need to know if this method can adapt its communication plan in real-time without needing a complete overhaul of the synthesis process.

Rosa: Exactly, and that leads us into thinking about how robust these synthesized structures are when we actually put them into the field versus just running simulations on a controlled testbed.

Michael C. A. Nestor, Jiaxin Wang, Fei Teng

Department of Electrical and Electronic Engineering, Imperial College London · Department of Electrical Engineering, Tsinghua University

eess.SY, cs.SY

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: Preprint submitted to Automatica

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

Importance score: 72/100

The gist: In distributed control schemes, designing an optimal communication topology that guarantees controller existence while balancing communication costs and control performance is crucial for practical

Key concepts

Co-Design Scheme
This approach optimizes the communication links and the controller gains together. It seeks a trade-off between minimizing communication costs (like link expenses) and minimizing the closed-loop control cost, ensuring both are optimized simultaneously under stability rules.
Control-Aware Topology Design
This sequential scheme designs the topology first using performance proxies to prioritize strong subsystem coupling. It then synthesizes a controller structure that respects this chosen topology, aiming for efficiency without explicitly optimizing the full closed-loop cost.
Mixed-Integer Semidefinite Programs (MISDPs)
These are mathematical optimization problems used to solve the design challenges. They combine continuous variables (like gains) with discrete variables (like link selections). Solving these programs guarantees that the resulting topology and controller structure meet stability, structural, and performance requirements.
H2-norm
This is a measure of control performance quantifying how well a system responds to disturbances. Minimizing the H2-norm in this context means designing a controller that provides the best possible closed-loop response while keeping communication costs low.

Terminology

Summary

In distributed control schemes, designing an optimal communication topology that guarantees controller existence while balancing communication costs and control performance is crucial for practical implementation. This paper investigates data-driven approaches to topology design by formulating both co-design and sequential schemes, allowing for the co-optimization of the communication structure and controller synthesis directly from data.

The gist: A data-driven co-design scheme can synthesize a communication topology and a structured H2 controller directly from data by minimizing a combined cost function subject to stability, structural, and performance constraints formulated as mixed-integer semidefinite programs (MISDPs).

Problem Formulation Classes

The paper categorizes the design approaches into three classes: control-agnostic, control-aware topology design, and co-design. The overall framework is composed of co-design and sequential design classes. Co-design involves selecting communication links and controller gains together over link costs and closed-loop control cost. In contrast, the control-aware approach selects links using a performance proxy and constraints that preserve feasibility for the subsequent controller synthesis without explicitly optimizing the controller over the closed-loop control cost.

Co-Design Scheme

The co-design problem, denoted as PCo-Des, aims to minimize a combined cost function subject to stability, structural, and performance constraints. The objective is formulated as:

min P≻0,K, ∆ J(P, K) + ΣM i,j=1 cij δij (7a)

This optimization seeks a trade-off between communication costs and control costs. Key constraints include:

  1. A data-driven stability constraint Cstab(P, K, D,M) to ensure closed-loop stability.

  2. A structural constraint Cstr(P, K, ∆) ensuring the controller has a structure consistent with the topology.

  3. A performance constraint Cperf(P, K, D,M) guaranteeing an upper bound on control performance (e.g., closed-loop H2-norm).

Control-Aware Topology Design Scheme

The sequential control-aware scheme couples the two design problems by applying relaxations used in controller design within the topology design process. This approach is formulated as PCtrl-Aw, which includes an optional performance constraint Cperf. The cost function for this stage reflects a desire to prioritize communication between more strongly coupled subsystems:

min X≻0,P˜com, ∆,L,G ΣM i,j=1 δij (cij − ηij) (21a)

where the coupling strength estimate is defined as ηij:= Θij2 Θii2. This formulation minimizes the sum of communication link costs and coupling estimates subtracted from them. The resulting optimization problem is characterized as an MISDP, where feasibility guarantees that the controller structure respects the selected communication topology and stabilizes the system dynamics.

Stability and Structure Constraints

The paper employs data-driven stability constraints based on extended state dynamics (4) derived from a Linear Fractional Transformation (LFT) representation (5a). Stability is guaranteed by an LMI constraint Cstab, which is formulated as:

EdE⊤d − X 0 0 + 0 I I 0⊤ P˜com 0 I I 0⋆ (A′G + B′L)⊤ (CzG + DzL)⊤ −χ≺ 0

where χ = (G + G⊤ − X). Furthermore, the structural constraint Cstr is encoded using sufficient conditions derived from prior work, such as:

δij = 0 ⇒ Eu,iLE⊤ξ,j = 0 ∀i, j (15a)

Numerical Examples and Results

The effectiveness of both schemes is demonstrated through simulations on the IEEE 14-bus power system. The results show that the co-design approach attains the lowest synthesis bounds and realized closed-loop cost compared to the control-aware design. Specifically, in a 4-subsystem chain example, co-design outperformed control-aware design in both computation time and performance metrics across varying noise bounds. The study also highlights that performance under control-aware topologies can degrade non-monotonically as the number of links increases due to conservatism introduced by structural constraints.

Conclusion

The paper presents a data-driven co-design MISDP that minimizes the sum of a certified upper bound on the squared closed-loop H2-norm and communication costs, achieving a balance between control performance and communication requirements. The control-aware approach provides an efficient alternative, often yielding competitive results with lower computation times than full co-design. However, the necessity of the condition pl = n limits the general real-world applicability of this specific data-driven synthesis method. Future work is suggested to relax this restriction and explore topology design within a distributed synthesis setting.

References

[1] V. Pichai, M. Sezer, and D.

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed this paper, Data-Driven Communication Topology and Distributed Controller Synthesis: Control-Aware and Co-Design, which focuses on integrating communication topology design with distributed controller synthesis for cyber-physical systems.

The paper proposes two main frameworks: a data-driven co-design scheme (optimizing topology and controller together) and a sequential control-aware scheme (designing topology first using control constraints, then synthesizing the controller). The core mathematical machinery relies on Mixed-Integer Semidefinite Programs (MISDPs) to handle the discrete nature of communication links.

Here are specific, high-impact improvements to AI systems that can be derived from this research:

I. Improvements in Distributed Control Systems (Cyber-Physical Systems)

The primary application domain is distributed control of interconnected systems (like power grids or complex networks).

  1. A system that can autonomously design the optimal communication network structure for a set of distributed agents to achieve a desired performance metric (e.g., minimizing H2 norm) while simultaneously synthesizing a stabilizing, sparse controller compatible with that topology.

  2. A control system capable of dynamically adapting its communication links in real-time based on current operational data and predicted coupling strengths between subsystems, thereby achieving optimal trade-offs between communication overhead and control accuracy.

  3. A robust distributed controller synthesis module that guarantees the existence of a stabilizing controller even when the underlying physical network topology is unknown or imperfect, provided the communication topology is designed to satisfy specific structural constraints (e.g., avoiding unstable Fixed Modes).

II. Improvements in Network Topology and Communication Design

These improvements leverage the novel data-driven metrics proposed in Section 5.2:

  1. A Communication Cost Minimization Engine that uses subspace predictive control (SPC) and robust regression on operational data to estimate the coupling strength between subsystems, allowing it to select communication links that maximize information flow efficiency relative to control needs (minimizing the objective function: Communication Cost - Coupling Benefit).

  2. A topology optimization algorithm that solves a complex MISDP where the objective is not just minimizing communication cost, but specifically maximizing the benefit derived from high-coupling interactions, leading to topologies that are both sparse (low communication cost) and functionally optimal for control performance.

III. Improvements in Computational Efficiency and Robustness

The paper highlights computational trade-offs:

  1. A Control-Aware Topology Designer that uses a sequential approach where the topology design stage incorporates controller synthesis relaxations (via stability constraints derived from the extended state dynamics) to ensure feasibility, resulting in significantly lower computation times compared to full co-design optimization for large systems.

  2. A certification layer within an AI control system that verifies, using Linear Matrix Inequalities (LMIs) and data-driven uncertainty sets (Assumptions 4 and 5), that the designed communication topology guarantees closed-loop stability under bounded measurement noise and process disturbances.

This research provides a powerful framework for moving beyond one-size-fits-all control architectures by allowing the network structure itself to become an optimized, data-informed design variable.

Abstract

In distributed control schemes, the communication topology that defines information sharing between agents must be designed prior to online control execution. Previous data-driven works typically decouple the topology and controller design problems. This paper investigates data-driven approaches to topology design that guarantee the existence of a stabilizing controller, whilst trading off the level of required communication against control performance. We formulate a data-driven co-design scheme that directly co-optimizes the topology and controller over the link costs and closed-loop control cost. When the topology designer does not have access to the agents' control objectives, we propose a control-aware approach that couples the two design problems within a sequential scheme. In both cases, the topology is optimized within a mixed-integer semidefinite program (MISDP). Simulations including the IEEE 14-bus power system test case demonstrate the effectiveness of both co-design and control-aware schemes. Numerical examples show lower computation time for the control-aware case than for co-design.

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