Hierarchical Control and Topology Co-Design for Networked Systems Using Model-Based and Data-Driven Approaches
Researchers propose hierarchical control design strategies for networked systems that can work with either known subsystem dynamics (model-based) or only input-output trajectory data (data-driven). The approach uses dissipativity theory and linear matrix inequality problems to ensure robust performance while optimizing network topology costs. This work addresses a practical gap in control design by enabling decentralized, composable solutions without requiring centralized, iterative optimization.
The paper presents two complementary hierarchical control design strategies for interconnected linear subsystems with disturbance inputs and performance outputs. The model-based approach leverages dissipativity theory to design local controllers that enforce local guarantees, which are then exploited to co-design distributed global controllers and network topology while maintaining compositionality and decentralizability through convex LMI optimization. Recognizing that real-world systems often lack complete dynamic knowledge, the authors also develop a data-driven variant that requires only rich input-state-output trajectory data and uses the matrix S-lemma to handle bounded disturbances. Both approaches avoid non-convex, iterative, centralized design processes that are computationally inefficient. The methods are validated on a DC microgrid application, demonstrating robust voltage regulation and current sharing capabilities.
What's missing
The paper does not discuss computational complexity comparisons between the proposed LMI-based approach and existing centralized or iterative methods, nor does it provide explicit convergence guarantees or sample complexity bounds for the data-driven strategy. The practical scalability limits of the approach to very large-scale networks are not addressed.
What different sources said
- arXiv cs.AICenter
Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems
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