The main objective of this work is to demonstrate some new computational methods
for estimation, optimization and modeling of dynamical systems that use automatic
differentiation. Particular focus will be upon dynamical systems arising in Aerospace
Engineering. Automatic differentiation is a recursive computational algorithm, which
enables computation of analytically rigorous partial derivatives of any user-specified
function. All associated computations occur, in the background without user
intervention, as the name implies. The computational methods of this dissertation are
enabled by a new automatic differentiation tool, OCEA (Object oriented Coordinate
Embedding Method). OCEA has been recently developed and makes possible efficient
computation and evaluation of partial derivatives with minimal user coding. The key
results in this dissertation details the use of OCEA through a number of computational
studies in estimation and dynamical modeling.
Several prototype problems are studied in order to evaluate judicious ways to use
OCEA. Additionally, new solution methods are introduced in order to ascertain the
extended capability of this new computational tool. Computational tradeoffs are studied
in detail by looking at a number of different applications in the areas of estimation,
dynamical system modeling, and validation of solution accuracy for complex dynamical
systems. The results of these computational studies provide new insights and indicate
the future potential of OCEA in its further development.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/1408 |
| Date | 17 February 2005 |
| Creators | Griffith, Daniel Todd |
| Contributors | Junkins, John L. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 11948932 bytes, electronic, application/pdf, born digital |
Page generated in 0.003 seconds