Semi-Autonomous Networks

Airlie Chapman

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

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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.
Auteur
Airlie Chapman received the Ph.D. degree from the William E. Boeing Aeronautics and Astronautics Department at the University of Washington, Seattle in 2013 and was simultaneously awarded the M.S. degree in mathematics. She received the B.S. degree in aeronautical (space) engineering and the M.S. degree in engineering research from the University of Sydney, Australia, in 2006 and 2008, respectively. She is currently a postdoctoral fellow at the University of Washington, Seattle. Dr. Chapman was awarded the College of Engineering Dean's Fellowship at the University of Washington and is a two-time recipient of the Amelia Earhart Fellowship. Her research interests are networked dynamic systems and graph theory with applications to robotics and aerospace systems.
Contenu
Nomenclature Acknowledgments Dedication Supervisor's Foreword IntroductionPreliminariesNotation Network TopologyConsensus DynamicsPart 1. Beyond Linear Consensus Chapter 1. Advection on Graphs 1.1. Introduction 1.2. Advection Properties 1.3. Examples 1.4. RemarksChapter 2. Beyond Linear Protocols2.1. Introduction2.2. Model2.3. Equilibria and Convergence2.4. Extension2.5. RemarksPart 2. Network Measures and Adaptive TopologiesChapter 3. Measures and Rewiring3.1. Introduction3.2. Leader-Follower Consensus Dynamics3.3. Mean Tracking Measure3.4. Variance Damping Measure3.5. Fusing Adaptive Protocols3.6. RemarksChapter 4. Distributed Online Topology Design for Disturbance Rejection4.1. Introduction4.2. Online Convex Optimization4.3. Model and Measure4.4. Distributed Online Topology Design Algorithm4.5. RemarksChapter 5. Network Topology Design for UAV Swarming with Wind Gusts5.1. Introduction5.2. Model5.3. Open Loop H2 Norm5.4. Topology Design5.5. RemarksPart 3. Cartesian Product Networks Chapter 6. Cartesian Products of Z-Matrix Networks: Factorization and IntervalAnalysis 6.1. Introduction6.2. Cartesian Product6.3. Z-matrix Dynamics6.4. Interval Matrices6.5. Z-Matrix Dynamics over Cartesian Products of Digraphs6.6. RemarksChapter 7. On the Controllability and Observability of Cartesian Product Networks7.1. Introduction7.2. Digraph Automorphisms7.3. Problem setup7.4. Control Product7.5. Layered Control7.6. Filtering on Social Product Networks7.7. RemarksPart 4. Structural ControllabilityChapter 8. Strong Structural Controllability of Networked Dynamics8.1. Introduction8.2. Pattern Matrices8.3. Model8.4. Structural Controllability8.5. Testing inputs for Strong S-Controllability8.6. Finding Strongly S-Controllable Inputs8.7. RemarksChapter 9. Security and Infiltration of Networks: A Structural Controllability andObservability Perspective9.1. Introduction9.2. Weak Structural Controllability - A cautious lower bound9.3. Strong Structural Controllability - Guaranteed Security9.4. RemarksFinal RemarksChapter 10. Conclusion and Future Work10.1. Concluding Remarks10.2. Future DirectionsAppendixSingle Anchor State Measures