Modeling Hubble Space Telescope Flight Data by Q-Markov Cover Identification

Abstract

This Paper presents a state space model for the Hubble space telescope under the influence of unknown disturbances in orbit. This model was obtained from flight data by applying the Q-Markov Covariance Equivalent Realization identification algorithm. This state space model guarantees the match of the first Q Markov parameters and covariance parameters of the Hubble system. The flight data were partitioned into high and low frequency components for more efficient Q-Markov Cover modeling, to reduce some computational difficulties of the Q-Markov Cover algorithm. This identification revealed more than 20 lightly-damped modes within the bandwidth of the attitude control system. Comparisons with the analytical (TREETOPS) model are also included.