New Directions for PDE Control- Safety, Learning, and Ensemble Stabilization
Abstract
In this project we pioneer or bring towards maturity three new directions in PDE control. (1) Safe control of PDEs, motivated by fluid, structural, traf�c, population dynamics, and other systems. (2) Machine learning-enhanced PDE control, which yields a thousandfold speedup in the computation of the gain functions for PDEs and enables for the �rst time the implementation of gain scheduling nonlinear controllers and adaptive controllers for PDEs. (3) Control of PDE ensembles, which are parametrized continuum-in�nite families of PDEs in many dimensions and arise in applications that range from multi-phase flows to epidemics and opinion dynamics. A signi�cant factor in the choice of topics will be the relevance to the Air Force, Space, and other national security platforms, informed by the PI’s accumulated exposure to industrial and national laboratory research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 06, 2024
- Source ID
- FA95502310535
Entities
People
- Miroslav Krstić
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California, San Diego