scispace - formally typeset
D

David S. Gilliam

Researcher at Texas Tech University

Publications -  118
Citations -  1674

David S. Gilliam is an academic researcher from Texas Tech University. The author has contributed to research in topics: Distributed parameter system & Nonlinear system. The author has an hindex of 20, co-authored 118 publications receiving 1560 citations. Previous affiliations of David S. Gilliam include Virginia Tech & University of Northern Colorado.

Papers
More filters
Journal ArticleDOI

Output regulation for linear distributed parameter systems

TL;DR: It is shown that the full state feedback and error feedback regulator problems are solvable, under the standard assumptions of stabilizability and detectability, if and only if a pair of regulator equations is solvable.
Journal ArticleDOI

The State Feedback Regulator Problem for Regular Linear Systems

TL;DR: Under suitable assumptions, the state feedback regulator problem for infinite-dimensional linear systems is shown to be solvable if and only if a pair of algebraic equations, called the regulator equations, is solvable.
Journal ArticleDOI

On the Global Dynamics of a Controlled Viscous Burgers' Equation

TL;DR: In this article, the authors consider a boundary control problem for a forced Burgers' equation in a Hilbert state space consisting of square integrable functions on a finite interval and prove the global in time existence of solutions of the closed loop boundary control system for arbitrary L 2 initial data and quite general forcing terms.
Journal ArticleDOI

Regular Linear Systems Governed by a Boundary Controlled Heat Equation

TL;DR: In this paper, the authors considered a class of distributed parameter systems governed by the heat equation on bounded domains in Bbb Rn and showed that any possible combination of the aforementioned inputs and outputs provides a regular linear system.
Journal ArticleDOI

Ethanol teratogenesis in five inbred strains of mice.

TL;DR: Differences among inbred strains demonstrate genetic variation in the teratogenic effects of ethanol in mice, allowing future studies to elucidate the genetic architecture underlying prenatal alcohol phenotypes.