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 constructive tools for the development of model-free and data-driven stochastic hybrid dynamical systems in the context of multi-agent systems, with stability, convergence, and robustness guarantees. To achieve this objective, we will rely on and enhance the PIs recent work on a general framework for stochastic hybrid dynamical systems and model-free hybrid control systems, which allows for non-unique solutions and thus the interplay between randomness and independent decision making in multi-agent systems. Our goal is to generate novel analytical tools for the analysis of data-driven algorithms in the context of stochastic hybrid dynamical systems, as well as constructive procedures for the design of robust model-free and data-driven algorithms, with performance guarantees, for networked multi-agent dynamical systems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 18, 2022
- Accession Number
- AD1230398
Entities
People
- Andrew R. Teel
Organizations
- University of California, Santa Barbara