Model Reduction using a Frequency-Limited H2-Cost
For control systems engineers, this provides a computationally efficient way to perform frequency-weighted model reduction without input/output filters, though it is an incremental improvement over existing frequency-weighted methods.
The paper proposes a model reduction method for a specified frequency range by minimizing a frequency-limited H2 cost function using nonlinear optimization, with a key contribution being the derivation of a computationally efficient gradient. The method enables the use of off-the-shelf optimization software.
We propose a method for model reduction on a given frequency range, without the use of input and output filter weights. The method uses a nonlinear optimization approach to minimize a frequency limited H2 like cost function. An important contribution in the paper is the derivation of the gradient of the proposed cost function. The fact that we have a closed form expression for the gradient and that considerations have been taken to make the gradient computationally efficient to compute enables us to efficiently use off-the-shelf optimization software to solve the optimization problem.