Online parameter estimation applied to mixed conduction/radiation /

Bibliographic Details
Main Author:	Shah, Tejas Jagdish, 1979-
Other Authors:	Beskok, Ali (Thesis advisor)
Format:	Thesis eBook
Language:	English
Published:	[College Station, Tex.] : [Texas A&M University], [2005]
Subjects:	Major mechanical engineering. Unscented Kalman Filter Kalman Filter thermal model Online Parameter Estimation Extended Kalman Filter
Online Access:	Link to OAK Trust copy

Description
Abstract:	The conventional method of thermal modeling of space payloads is expensive and cumbersome. Radiation plays an important part in the thermal modeling of space payloads because of the presence of vacuum and deep space viewing. This induces strong nonlinearities into the thermal modeling process. There is a need for extensive correlation between the model and test data. This thesis presents Online Parameter Estimation as an approach to automate the thermal modeling process. The extended Kalman fillter (EKF) is the most widely used parameter estimation algorithm for nonlinear models. The unscented Kalman filter (UKF) is a new and more accurate technique for parameter estimation. These parameter estimation techniques have been evaluated with respect to data from ground tests conducted on an experimental space payload.
Item Description:	"Major Subject: Mechanical Engineering" Title from author supplied metadata (automated record created on Sep. 21, 2005.) Vita. Abstract. Electronic resource.
Format:	Mode of access: World Wide Web. System requirements: World Wide Web access and Adobe Acrobat Reader.
Bibliography:	Includes bibliographical references.