Hybrid Data-Driven Algorithms for Networked Multi-Agent Systems: Stability and Robustness
Abstract
The main goal of the proposed research is to develop analytical and constructivetools for the development of model-free and data-driven stochastic hybriddynamical systems in the context of multi-agent systems, with stability, convergence,and robustness guarantees. To achieve this objective, we will rely on and enhance thePI’s recent work on a general framework for stochastic hybrid dynamical systems andmodel-free hybrid control systems, which allows for non-unique solutions and thusthe interplay between randomness and independent decision making in multi-agentsystems. Our goal is to generate novel analytical tools for the analysis of data-drivenalgorithms in the context of stochastic hybrid dynamical systems, as well as constructiveprocedures for the design of robust model-free and data-driven algorithms,with performance guarantees, for networked multi-agent dynamical systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 11, 2018
- Source ID
- FA95501810246
Entities
People
- Andrew R. Teel
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California, Santa Barbara