Online parameter estimation applied to mixed conduction/radiation

Simple item page
dc.contributorBeskok, Ali
dc.creatorShah, Tejas Jagdish
dc.date.accessioned2005-08-29T14:39:45Z
dc.date.accessioned2017-04-07T19:50:11Z
dc.date.available2005-08-29T14:39:45Z
dc.date.available2017-04-07T19:50:11Z
dc.date.created2006-05
dc.date.issued2005-08-29
dc.description.abstractThe 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.
dc.identifier.urihttp://hdl.handle.net/1969.1/2361
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectOnline Parameter Estimation
dc.subjectKalman Filter
dc.subjectUnscented Kalman Filter
dc.subjectExtended Kalman Filter
dc.subjectthermal model
dc.titleOnline parameter estimation applied to mixed conduction/radiation
dc.typeThesis

Files

Collections

Texas A&M University at College Station