Scalable Control and Verification with Compositional, Hierarchical and Learning-Based Methods
Abstract
This project will develop scalable control and veri cation tools for systems with high dimensional, nonlinear, uncertain dynamics, and complex requirements. It will achieve scalability with a con uence of compositional, hierarchical and learning-based approaches. The compositional approach exposes a large system as an interconnection of smaller subsystems and derives system-level guarantees from appropriate abstractions of the subsystems. The hierarchical approach decomposes the synthesis and veri fication tasks into layers, from high-level decision making to low-level control synthesis. Taken together, these approaches break apart intractable problems into subproblems of manageable size. In addition they provide the designer with modularity, as inter- faces between subsystems and layers demarcate the system into components that can be modifi ed individually while preserving high-level guarantees.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110288XX0
Entities
People
- Murat Arcak
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California Regents