Generalized Krylov Subspace-Based MOR of Periodic Systems

Numerical simulation plays a vital role in representing the large-scale dynamical systems that are generated from complex physical phenomena. Naturally, simulations of large-scale models are computationally costly and time-demanding, which is not desirable in many important applications of control and optimization. Model order reduction (MOR) is the tool that reduces this computational burden by replacing the large-scale model with a much smaller one that is faster and easier to simulate. The MOR of index-2 linear discrete-time periodic system (LDTP) applying the generalized Krylov subspace approach has been suggested in this paper. The reduced-order model has been computed by applying a projection technique using the generalized Arnoldi method. In this case, it has been ensured that the periodic structure is preserved in the periodic Arnoldi approximation. This study also includes numerical results to demonstrate the efficacy and accuracy of the proposed algorithm.