INTEGRATED SYSTEM-IDENTIFICATION AND STATE ESTIMATION FOR CONTROL OF FLEXIBLE SPACE STRUCTURES

A novel approach is developed for identification of a state-space model and a Kalman filter gain from input and output data. There are four steps involved in this approach. First, the relation between a stochastic state-space model of a dynamical system and the coefficients of its autoregressive model with exogeneous input is derived. Second, an adaptive least-squares transversal predictor is used to estimate the coefficients of the model. Third, a state-space model and a steady-state Kalman filter gain of the dynamical system are then identified from the coefficients of the model by using the eigensystem realization algorithm. Fourth, a modal state estimator is constructed using the modal parameters of the identified model. On-line implementation of this algorithm can continually improve the modal parameters and the filter gain. It can also gradually update the system model when the system characteristics are slowly changing. Numerical examples are used to illustrate the feasibility of the new approach.