Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems
arXiv:2606.11596v1 Announce Type: cross Abstract: 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 interco
The increasing complexity and interconnectedness of modern systems, particularly in AI and automation, necessitate more robust and scalable control mechanisms. This research addresses the demand for advanced control strategies to manage these intricate networks.
A strategic reader should care because improving the robustness and resilience of networked systems through hierarchical control directly impacts critical infrastructure, autonomous systems, and defense applications. Enhanced system stability and performance can prevent cascading failures and optimize operational efficiency.
This research introduces a co-design approach for hierarchical control and network topology using dissipativity theory, offering a more integrated and theoretically grounded method for building reliable complex systems. It moves beyond isolated local control designs to consider system-wide robustness from the outset.
- · AI and automation sectors
- · Defense contractors
- · Infrastructure operators
- · Control systems engineers
- · Developers of brittle or poorly integrated networked systems
- · Legacy control system providers
More resilient and efficient networked control systems can be developed, reducing operational failures and maintenance costs.
This foundational research could accelerate the deployment of complex autonomous systems in critical applications like smart grids or advanced robotics.
The principles might be extended to secure and manage distributed AI agents or even national cyber infrastructure against sophisticated attacks, enhancing overall systemic robustness.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI