Passive reduced order modelling of second-order systems

Two reduction methods for passive second-order systems are presented, preserving not only the second-order structure but also the passivity of the original system. The first method is based on applying a projection directly to the second-order system using a second-order Krylov subspace. To increase the number of matching parameters, in the second approach, it is proposed to reduce the equivalent state space model by a one-sided Krylov method and to extract the second-order matrices by applying a similarity transformation to the reduced system.

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