Abstract
An approach to estimate spacecraft system parameters is proposed in this paper. Oftentimes, spacecraft is under uncertainties of the system as well as internal and external disturbances which have different aspects each other. The moment of inertia of spacecraft is unknown especially when it is under considerable fuel consumption or equipped with deployable structures. This study aims to estimate the moment of inertia of the spacecraft body as well as its attitude and angular rate by using predictive filtering algorithm. Crassidis and Markley developed a predictive filtering algorithm for nonlinear estimation under a large system model error. This approach focuses not on the specific sources of model error described in the equations of motion but the resultant model error vector driving errors on the system dynamics. Therefore, we are not able to update the system model since the estimated model error has no additional information about the system. This paper establishes a method to apply the predictive filtering algorithm for nonlinear spacecraft parameter estimation by defining a new model error vector for parameters. This study shows different sources of the model error can be separated for estimation, and reveals excellent estimation results. Proposed algorithm is verified by numerical simulation studies.
Published Version
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