Date of Award
Spring 5-15-2023
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Large-scale dynamic population systems are prevalent in diverse domains at different scales from cell to society. Robust control of such ensemble systems, consisting of a vast collection of isolated or interconnected, nearly identical dynamical units is compelling to enable many cutting-edge applications, such as nuclear magnetic resonance spectroscopy and imaging in quantum control, pinning control in complex networks, and neural stimulation in brain medicine. Dynamic network systems, which consist of coupled dynamical systems, are also frequently investigated for emerging problems in finance, social science, public health, and more. In this thesis, moment-based and heuristic approaches are proposed for systems-theoretic analysis and design of dynamic population systems. We introduce a moment-based method for controllability analysis and control design of ensemble systems. The method establishes an equivalence between ensemble dynamics and its associated moment dynamics. The banded structure of the derived moment dynamics is exploited to create a unified ensemble control design framework that steers the ensemble systems toward the targets. For dynamic network systems, we examine pinning control problems for stabilizing the network dynamics. We propose systematic techniques for identifying the best pinning locations to achieve maximal stability by crafting heuristic centrality measures from the network coupling topology for tree and cyclic networks, respectively.
Language
English (en)
Chair
Jr-shin Li
Committee Members
ShiNung Ching, Istvan Z. Kiss, Joseph O'Sullivan, Shen Zeng,