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Online parameter estimation applied to mixed conduction/radiation

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.

Identiferoai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2361
Date29 August 2005
CreatorsShah, Tejas Jagdish
ContributorsBeskok, Ali
PublisherTexas A&M University
Source SetsTexas A and M University
Languageen_US
Detected LanguageEnglish
TypeElectronic Thesis, text
Format3322196 bytes, electronic, application/pdf, born digital

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