Identification of low-order models using Laguerre basis function expansions

In this paper, a method for identification of low-order output-error models expressed in terms of Laguerre basis functions is presented. The identification problem is formulated as a rank-constrained optimization problem in the Laguerre domain, which can be solved efficiently using nuclear norm regularization. Since the identified models are of low order, a minimal state-space realization of the system can be obtained without the use of additional approximative model-order reduction steps.