An Optimal Projection Framework for Structure-Preserving Model Reduction of Linear Systems

This paper presents a structure-preserving model reduction framework for linear systems, in which the $\mathcal{H}_2$ optimization is incorporated with the Petrov-Galerkin projection to preserve structural features of interest, including dissipativity, passivity, and bounded realness.

The model reduction problem is formulated in a nonconvex optimization setting on a noncompact Stiefel manifold, aiming to minimize the $\mathcal{H}_2$ norm of the approximation error between the full-order and reduced-order models.

The explicit expression for the gradient of the objective function is derived, and two gradient descent procedures are applied to seek for a (local) minimum, followed by a theoretical analysis on the convergence properties of the algorithms.

Finally, the performance of the proposed method is demonstrated by two numerical examples which consider stability-preserving and passivity-preserving model reduction problems, respectively.