INTELLIGENT DISTRIBUTED SYSTEMS
Abstract
We propose to develop asynchronous algorithms for estimating the state of a dynamical system over a distributed time-vary agent network in the face of modeling errors. We will exploit recent advances in distributed estimation which enable one to “split” certain distributed estimators into two parts - one for which conventional tools can be used to adjust convergence rates and the other for which convergence rate can be controlled by switching and averaging. Using ideas from parameteradaptive control, we will attempt to deal with the inherent lack of robustness to modeling errors exhibited by most state estimators for unstable processes. We propose to addresses the question of how to reliably perform computations in a distributed manner to determine shared fixed points of families of functions which arise in distributed optimization and parameter estimation problems. Using recent ideas regarding the intersection of convex sets, we will explore possible approaches to defending a distributed fixed point calculator against malicious attack.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010037
Entities
People
- A. Stephen Morse
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- Yale University