Controllability Identification And Randomness In Distributed Systems

Controllability, Identification, and Randomness in Distributed Systems

This interdisciplinary thesis involves the design and analysis of coordination algorithms on networks, identification of dynamic networks and estimation on networks with random geometries with implications for networks that support the operation of dynamic systems, e.g., formations of robotic vehicles, distributed estimation via sensor networks. The results have ramifications for fault detection and isolation of large-scale networked systems and optimization models and algorithms for next generation aircraft power systems. The author finds novel applications of the methodology in energy systems, such as residential and industrial smart energy management systems.

Controllability, Identification, and Randomness in Distributed Systems

Controllability of Dynamic Systems

The book is about the possibilities of involvement of the well-known Green’s function method in exact or approximate controllability analysis for dynamic systems. Due to existing extensions of the Green’s function notion to nonlinear systems, the approach developed here is valid for systems with both linear and nonlinear dynamics. The book offers a number of particular examples, covering specific issues that make the controllability analysis sophisticated, such as coordinate dependent characteristics, point sources, unbounded domains, higher dimensions, and specific nonlinearities. It also offers extensive numerical analysis, which reveals both advantages and drawbacks of the approach. As such, the book will be of interest to researchers interested in the theory and practice of control, as well as PhD and Master’s students.