Dynamics and Control of Switching Dynamical Networks

Abstract

The research objective of this grant is to investigate the influence of network structure on the dynamics of complex networks with a time-varying stochastic coupling. This grant focuses on the rigorous mathematical analysis and control of oscillatory networks with connections that switch stochastically but switching is not necessarily fast and can be a Markov process, instead of sequences of independent random vectors. The development of a general rigorous theory of adaptive dynamical networks with on-off stochastic connections beyond fast switching constitutes the main research component of the grant. This grant will contribute to a unified high-dimensional analytical approach to realistic switching networks of different nature that co-evolve and adapt with the dynamics of the underlying topology. The following fundamental questions, related to control, performance and design of networks, will be studied: (i) How does the switching frequency affect the dynamical stability of the network and the convergence to a desired performance objective? (ii) What is the role of spatial and temporal correlations of switching events in optimal performance of switching networks? (iii) What are the critical feedback mechanisms linking the adaptive structure and dynamics of switching networks? (iv) How effectively can the dynamics of a switching network be controlled by adaptively pinning a fraction of its nodes? A number of practical applications of adaptive networks will be studied in this context. The theoretical framework, developed in the project, will be tested in three experimental setups such as spanning robotics, human-machine interactions, and animal grouping. Testing the theoretical results will guide the formulation and development of mathematical tools on the basis of practical constraints and offer real-world validation of the feasibility of tailoring network evolution for optimal performance with impact within and beyond engineering. This project will also facilitate the transition of the project outcomes to other science and engineering disciplines, and contribute to the interdisciplinary training of the graduate students involved in the project.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 12, 2017
Source ID
W911NF1510267

Entities

People

  • Igor Belykh

Organizations

  • Army Contracting Command
  • Georgia State University
  • United States Army

Tags

Fields of Study

  • Computer science

Readers

  • Electrical Engineering
  • Neural Network Machine Learning.
  • Research Science/Academic Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control