A Subspace‐Based Method for Solving Lagrange–Sylvester Interpolation Problems

In this paper, we study the Lagrange-Sylvester interpolation of rational matrix functions which are analytic at infinity, and propose a new interpolation algorithm based on the recent subspace-based identification methods. The proposed algorithm is numerically efficient and delivers a minimal interpolant in state-space form. The solvability condition for the subspace-based algorithm is particularly simple and depends only on the total multiplicity of the interpolation nodes. As an application, we consider subspace-based system identification with interpolation constraints, which arises, for example, in the identification of continuous-time systems with a given relative degree.