MODELING HUBBLE-SPACE-TELESCOPE FLIGHT DATA BY Q-MARKOV COVER IDENTIFICATION

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 night data by applying the Q-Markov Covariance Equivalen t Realization (Q-Markov Cover) identification algorithm. This state-sp ace model guarantees the match of the first Q-Markov parameters and co variance parameters of the Hubble system. The flight data were partiti oned into high- and low-frequency components for more efficient Q-Mark ov Cover modeling to reduce some computational difficulties of the Q-M arkov Cover algorithm. This identification revealed more than 20 light ly damped modes within the bandwidth of the attitude control system. C omparisons with the analytical (TREETOPS) model are also included.