Hierarchical Control and Topology Co-Design for Networked Systems Using Model-Based and Data-Driven Approaches
A new paper submitted to arXiv introduces a hierarchical control and topology co-design framework for networked linear systems that works both when subsystem dynamics are known and when only trajectory data is available. The approach uses dissipativity theory and linear matrix inequalities (LMIs) to guarantee robust performance while optimizing interconnection topology costs. The work is significant because it avoids non-convex, centralized design processes and demonstrates applicability to DC microgrid voltage regulation and current sharing.
Researchers have proposed a dual-mode hierarchical control design strategy for networked systems composed of interconnected linear subsystems with disturbance inputs and performance outputs. The model-based approach leverages dissipativity theory to design local controllers that enforce local dissipativity guarantees, which are then used to co-design distributed global controllers and the network interconnection topology via a sequence of LMI problems. This compositional, decentralizable process avoids the inefficiencies of non-convex and iterative centralized methods. Recognizing that subsystem dynamics are often unknown in practice, the authors also develop a data-driven variant that relies solely on input-state-output trajectory data, handling unknown bounded disturbances through a quadratic matrix inequality relaxation and the matrix S-lemma. Both strategies are validated on a DC microgrid benchmark, targeting robust voltage regulation and current sharing. The paper is currently a preprint submitted to the journal Automatica and has not yet undergone peer review.
What's missing
The paper is a preprint and has not yet been peer-reviewed. Key open questions include scalability to large-scale or nonlinear networked systems, sensitivity of the data-driven approach to data quality and quantity, and how the quadratic matrix inequality disturbance bound compares empirically to conventional bounds in realistic settings. Computational complexity of the LMI sequences for large networks is not fully characterized.
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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