# Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems

arXiv: https://arxiv.org/abs/2606.11596 · HTML: https://mlsanity.com/paper/2606.11596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Formulas for Data-driven Control: Stabilization, Optimality and Robustness](https://mlsanity.com/paper/1903.06842/) (2019)
- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [Data-Driven Model Predictive Control with Stability and Robustness Guarantees](https://mlsanity.com/paper/1906.04679/) (2019)
- [Distributed Linear-Quadratic Control with Graph Neural Networks](https://mlsanity.com/paper/2103.08417/) (2021)
- [From noisy data to feedback controllers: non-conservative design via a matrix S-lemma](https://mlsanity.com/paper/2006.00870/) (2020)
- [Distributed Synthesis of Local Controllers for Networked Systems with Arbitrary Interconnection Topologies](https://mlsanity.com/paper/1902.10506/) (2019)
- [Consensus Planning with Primal, Dual, and Proximal Agents](https://mlsanity.com/paper/2408.16462/) (2024)
- [Reinforcement Learning based Distributed Control of Dissipative Networked Systems](https://mlsanity.com/paper/2011.14263/) (2020)
- [A Matrix Finsler's Lemma with Applications to Data-Driven Control](https://mlsanity.com/paper/2103.13461/) (2021)
- [Stochastic phase-cohesiveness of discrete-time Kuramoto oscillators in a frequency-dependent tree network](https://mlsanity.com/paper/1903.09676/) (2019)
- [Data-Driven Linear Quadratic Optimization for Controller Synthesis with Structural Constraints](https://mlsanity.com/paper/1912.03616/) (2019)
- [Nonlinear Adaptive Cruise Control of Vehicular Platoons](https://mlsanity.com/paper/2007.07054/) (2020)
- [On-line Estimation of Stability and Passivity Metrics](https://mlsanity.com/paper/2204.00073/) (2022)
- [A Decentralized Analysis and Control Synthesis Approach for Networked Systems with Arbitrary Interconnections](https://mlsanity.com/paper/2204.09756/) (2022)
- [Distributed Adaptive Backstepping Control for Vehicular Platoons with Mismatched Disturbances Using Vector String Lyapunov Functions](https://mlsanity.com/paper/2207.02101/) (2022)
- [Centralized and Decentralized Techniques for Analysis and Synthesis of Non-Linear Networked Systems](https://mlsanity.com/paper/2209.14552/) (2022)
- [Inventory Consensus Control in Supply Chain Networks using Dissipativity-Based Control and Topology Co-Design](https://mlsanity.com/paper/2502.06580/) (2025)
- [Dissipativity-Based Distributed Control and Communication Topology Co-Design for DC Microgrids with ZIP Loads](https://mlsanity.com/paper/2503.21042/) (2025)

## Abstract (arXiv)

In this paper, we consider a class of networked systems comprising an interconnected set of linear subsystems, disturbance inputs, and performance outputs. Using dissipativity theory, we first propose a model-based hierarchical control design strategy to ensure the closed-loop networked system is dissipative from its disturbance inputs to performance outputs. This involves designing local controllers for each subsystem to enforce local dissipativity guarantees, which are then exploited to co-design distributed global controllers and the interconnection topology to enforce global dissipativity guarantees while optimizing interconnection topology costs. The overall design process requires only solving a sequence of linear matrix inequality (LMI) problems, thereby retaining compositionality and decentralizability while avoiding non-convex, iterative design processes that are inefficient and centralized. This model-based hierarchical control design strategy assumes the knowledge of the subsystem dynamics, which may not hold in many real-world networked systems. Motivated by this, we also propose a data-driven hierarchical control design strategy that assumes only the availability of rich input-state-output trajectory data from the subsystems. The proposed data-driven design process assumes that the unknown disturbances affecting the subsystem dynamics are bounded by a quadratic matrix inequality (relaxing conventional bounds) and accounts for this by using the matrix S-lemma. Finally, the effectiveness of the proposed model-based and data-driven hierarchical control designs is illustrated for a networked system representing a DC microgrid, with the aim of enforcing robust (dissipative) voltage regulation and current sharing.