Mechanical Engineering ETDs

Publication Date

Summer 7-29-2025

Abstract

This dissertation investigates synchronization, transient dynamics, control, and signal amplification in networked dynamical systems. First, it examines complete and cluster synchronization, including cases with large parametric mismatches, in both natural (e.g., brain networks, fireflies) and technological systems (e.g., power grids, robotics). Second, it studies transient dynamics in consensus processes, designing synchronization strategies that link reactivity, contraction theory, and Lyapunov exponents. Third, it addresses the computational limits of Model Predictive Control (MPC) for large networks by introducing an algebraic decomposition method that enables parallel online computation of smaller subproblems, enhancing MPC performance under hardware constraints. Finally, it analyzes the structural role of networks in amplifying or blocking environmental signals using the H2-norm. Empirical evidence suggests many natural systems are organized to enhance signal passing, with directed acyclic graphs showing amplification patterns dependent on the number and length of input–output paths, reflecting a tendency toward DAG-like structures in real-world networks.

Keywords

Complex Networks, Model Predictive Control, Nonlinear Dynamics, Transient Analysis

Degree Name

Mechanical Engineering

Level of Degree

Doctoral

Department Name

Mechanical Engineering

First Committee Member (Chair)

Francesco Sorrentino

Second Committee Member

Zahra Aminzare

Third Committee Member

David Phillips

Fourth Committee Member

Wenbin Wan

Document Type

Dissertation

Language

English

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