Abstract
ABSTRACTThe eigensystem realization algorithm (ERA) is a commonly used data-driven method for system identification and reduced-order modelling of dynamical systems. The main computational difficulty in ERA arises when the system under consideration has a large number of inputs and outputs, requiring to compute a singular value decomposition (SVD) of a large-scale dense Hankel matrix. In this work, we present an algorithm that aims to resolve this computational bottleneck via tangential interpolation. This involves projecting the original impulse response sequence onto suitably chosen directions. The resulting data-driven reduced model preserves stability and is endowed with an a priori error bound. Numerical examples demonstrate that the modified ERA algorithm with tangentially interpolated data produces accurate reduced models while, at the same time, reducing the computational cost and memory requirements significantly compared to the standard ERA. We also give an example to demonstrate the limitations of the proposed method.
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