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

2026-06-10 · Source: Artificial Intelligence · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new research proposes model-based and data-driven hierarchical control and topology co-design strategies for robust networked systems. These strategies aim to ensure closed-loop dissipativity from disturbance inputs to performance outputs in interconnected linear subsystems. The model-based approach utilizes dissipativity theory to design local and global controllers, alongside optimizing interconnection topology, by solving a sequence of linear matrix inequality (LMI) problems. This method maintains compositionality and decentralizability. For scenarios lacking subsystem dynamics knowledge, a data-driven strategy is introduced, relying solely on input-state-output trajectory data. This data-driven method accounts for unknown disturbances bounded by a quadratic matrix inequality using the matrix S-lemma. Both designs are demonstrated on a DC microgrid to enforce robust voltage regulation and current sharing.

Key takeaway

For control systems engineers designing robust networked systems, consider integrating hierarchical control and topology co-design. If subsystem dynamics are known, utilize the LMI-based model-based approach for compositional and decentralized solutions. When dynamics are unknown, explore the data-driven strategy using input-state-output data and the matrix S-lemma to manage disturbances. This can significantly enhance robust voltage regulation and current sharing in applications like DC microgrids.

Key insights

Hierarchical control and topology co-design ensure robust dissipativity in networked systems using model-based or data-driven methods.

Principles

• Dissipativity theory enables robust control design.
• Hierarchical control allows compositional and decentralized design.
• Topology co-design optimizes network interconnection costs.

Method

The model-based approach solves sequential LMI problems. The data-driven method uses input-state-output data and the matrix S-lemma to handle unknown disturbances.

In practice

• Apply to DC microgrids for robust voltage regulation.
• Implement for current sharing in networked power systems.

Topics

• Networked Systems
• Hierarchical Control
• Topology Co-Design
• Dissipativity Theory
• Linear Matrix Inequality
• Data-Driven Control
• DC Microgrids

Best for: AI Scientist, Research Scientist

Related on AIssential

• Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks — Spectral GNNs in RL enable real-time, scalable outage management in smart grids by capturing global network topology.
• Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment — CCSS-RS provides a robust, data-driven simulator for WWTPs, enabling long-horizon scenario screening despite challenging data conditions.
• A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems — A self-adaptive, learning-driven framework ensures robust DC arc-fault detection in PV systems despite hardware and environmental variability.
• Learning Control-Affine Reduced-Order Models via Autoencoders — Simultaneous training of autoencoders and state-space models enables robust control-affine reduced-order model identification.
• Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters — ND-MARL enables scalable, communication-aware quadcopter consensus control through a distributed, hierarchical reinforcement learning framework.
• A Nonlinear Separation Principle: Applications to Neural Networks, Control and Learning — A nonlinear separation principle ensures stability in RNNs for control and implicit deep learning.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.