Contraction Theory for Network Systems: Stability, Control and Learning
Abstract
This proposal focuses on the development of contraction theory and its application to modeling, analyzing, controlling, optimizing, and learning the dynamics of complex systems. This proposal argues that modern geometric contraction theory is emerging as a powerful framework with the potential to (i) provide a unifying approach to a broad range of applications and (ii) substantially push the boundaries of what can be rigorously analyzed and efficiently learned in complex systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502210059XX0
Entities
People
- Francesco Bullo
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California, Santa Barbara