Semi-Autonomous Networks: Effective Control of Networked Systems through Protocols, Design, and Modeling

Abstract

The objective of this dissertation is to explore the analysis and design of controlled networked dynamic systems - dubbed semi-autonomous networks. This work approaches the problem of effective control of semi-autonomous networks from three fronts: protocols which are run on individual agents in the network; the network interconnection topology design; and efficient modeling of these often large-scale networks. Central to our analysis is the examination of the role of network structure in the system dynamics. Exploiting this relationship, we extended the popular consensus protocol to advection and nonlinear consensus. Reformulating various network measures in terms of the network topology provided a method to adaptively improve performance by rewiring and reweighting the network topology. The network redesign algorithms were supported by a game-theoretic and an online learning regret analysis. We formulated a novel Cartesian product composition and decomposition method, providing stability and controllability guarantees for large-scale networks based on its smaller Cartesian factors. Abstracting away the specifics of the protocol, we characterized the controllability of a network based purely on the network interconnections alone using weak and strong structural controllability.