1.

Record Nr.

UNINA9910300431503321

Autore

Chapman Airlie

Titolo

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

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015

ISBN

3-319-15010-3

Edizione

[1st ed. 2015.]

Descrizione fisica

1 online resource (207 p.)

Collana

Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053

Disciplina

629.895630151563

Soggetti

Physics

Automatic control

Applications of Graph Theory and Complex Networks

Control and Systems Theory

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

"Doctoral Thesis accepted by University of Washington."

Nota di bibliografia

Includes bibliographical references at the end of each chapters and index.

Nota di contenuto

Nomenclature -- Acknowledgments -- Dedication -- Supervisor's Foreword -- Introduction -- Preliminaries -- Notation -- Network Topology -- Consensus Dynamics -- Advection on Graphs -- Beyond Linear Protocols -- Measures and Rewiring -- Distributed Online Topology Design for Disturbance Rejection -- Network Topology Design for UAV Swarming with Wind Gusts -- Cartesian Products of Z-Matrix Networks: Factorization and Interval Analysis -- On the Controllability and Observability of Cartesian Product Networks -- Strong Structural Controllability of Networked Dynamics -- Security and Infiltration of Networks: A Structural Controllability and Observability Perspective -- Conclusion and Future Work -- Appendix -- Single Anchor State Measures.

Sommario/riassunto

This thesis analyzes and explores the design of controlled networked dynamic systems - dubbed semi-autonomous networks. The 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. The author



extended the popular consensus protocol to advection and nonlinear consensus.  The network redesign algorithms are supported by a game-theoretic and an online learning regret analysis.