Online parameter estimation applied to mixed conduction/radiation
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.
Subject
Online Parameter EstimationKalman Filter
Unscented Kalman Filter
Extended Kalman Filter
thermal model
Citation
Shah, Tejas Jagdish (2006). Online parameter estimation applied to mixed conduction/radiation. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /2361.