This thesis focuses on decentralized deadbeat output regulation of discrete-time nonlinear plants that are composed of multiple agents. These agents interact, via scalar-valued signals, in a known structured way represented with a graph. This work is motivated by applications where it is infeasible and/or undesirable to introduce control action within each plant agent; instead, control agents are introduced to interact with certain plant agents, where each control agent focuses on regulating a specific plant agent, called its target. Then, two analyses are carried out to determine if regulation is achieved: targeting analysis is used to determine if control laws can be found to regulate all target agents, then growing analysis is used to determine the effect of those control laws on non-target plant agents. The strength of this novel approach is the intuitively-appealing notion of each control agent focusing on the regulation of just one plant agent. This work goes beyond previous research by generalizing the class of allowable plant dynamics, considering not only arbitrary propagation times through plant agents, but also allowing for non-symmetrical influence between the agents. Moreover, new necessary and sufficient algebraic conditions are derived to determine when targeting succeeds. The main contribution of this work, however, is the development of new easily-verifiable conditions necessary for targeting and/or growing to succeed. These new conditions are valuable due to their simplicity and scalability to large systems. They concern the positioning of control agents and targets as well as the propagation time of signals through the plant, and they help significantly with design decisions. Various graph structures (such as queues, grids, spiders, rings, etc.) are considered and for each, these conditions are used to develop a control scheme with the minimum number of control agents needed.

In this study, we present the implementation and experimental analysis of a modeling approach for analyzing tactical level land combat to generate information for weapon and ammunition planning. The discrete-time stochastic model (DSM), which can handle small and moderately large force levels, is based on single shot kill probabilities. Forces are assumed to be heterogeneous on both sides, and both directed and area fire types are modeled by means of combinatorial analysis. DSM considers overkills and can handle noncombat loss and engagement processes, discrete reinforcements, force combinations and divisions. In addition to experimenting with DSM, we estimate attrition rate coefficients used in Lanchester combat models, such that the two models will yield similar figures for force levels throughout the combat.

Thesis (Ph.D.) -- University of Western Sydney, [2002]. / "A thesis presented to the University of Western Sydney in partial fulfillment of the requirements for the degree of Doctor of Philosophy " Bibliography: leaves 254-258.

Thesis (Ph.D.). / Written for the School of Computer Science. Title from title page of PDF (viewed 2008/02/12). Includes bibliographical references.

Thesis (Ph.D.)--University of Western Australia, 2007.

Thesis (M.Info.Tech.-Res.)--University of Wollongong, 2008. / Typescript. Includes bibliographical references: leaf 234-245.

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Typescript. Includes bibliographical references (leaves 59-64).

Thesis (M.C.S.) - Carleton University, 2007. / Includes bibliographical references (p. 76-78). Also available in electronic format on the Internet.

Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (p. 111-114). Also available via the World Wide Web.

Peter M. Thompson, Gunter Stein, Michael Athans. / "July, 1983." Caption title. / Bibliography: p. 82. / NASA Grant NGL-22-009-124