Efficient model reduction of large scale systems using Krylov-subspace iterative methods

In this work, the use of Krylov-subspace iterative methods is examined to obtain low-rank approximate solutions of Lyapunov equations for use in contol-oriented model reduction of large space structures. In particular, we examine the application of these methods to the approximate component cost analysis (CCA) of large scale systems. It is shown that using the proposed methods approximate CCA reduced-order models can be obtained with a significant reduction in the computational effort and time. In addition, we derive closed-form expressions for approximate cost-equivalent, cost-decoupled and covarianceequivalent realizations using the proposed Krylov-subspace solutions.