NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·Jun 11·5 min read
Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems
TL;DR
Networked systems often struggle with robustness due to dynamic environments and uncertainties. A new hierarchical control framework, combining model-based and data-driven approaches, was developed to enhance system resilience.
✦ Why It Matters
Engineers can implement this hierarchical control framework to enhance the robustness of their networked systems.
Key Takeaways
- Utilize dissipativity theory for robust control in networked systems.
- Design local controllers to ensure subsystem stability.
- Apply data-driven methods when subsystem dynamics are unknown.
- Optimize interconnection topology to reduce costs while maintaining performance.
How It Works
The proposed hierarchical control design begins with local controllers for each subsystem, ensuring they meet dissipativity criteria. These local controllers are then integrated to form global controllers that maintain overall system stability.
The model-based approach requires knowledge of subsystem dynamics, while the data-driven approach uses trajectory data to infer system behavior, allowing for robust control without explicit models.