Data driven local coordinates for multivariable linear systems and their application to system identification (original) (raw)

In this paper we introduce a new parametrization for state-space systems: data driven local coordinates (DDLC). The parametrization is obtained by restricting the full state-space parametrization, where all matrix entries are considered to be free, to an a ne plane containing a given nominal state-space realization. This a ne plane is chosen to be perpendicular to the tangent space to the manifold of observationally equivalent state-space systems at the nominal realization. The application of the parametrization to prediction error identiÿcation is exempliÿed. Simulations indicate that the proposed parametrization has numerical advantages as compared to e.g. the more commonly used observable canonical form. ?