On optimal input signal design for identification of output error models

This paper extends recent results on minimum variance input signal design for identification of Finite Impulse Response (FIR) models to the Output Error (OE) system identification case. The idea is to use “the useful input parametrization” for OE models proposed by Stoica and Söderstrom (1982). The advantage of this parametrization is that the Toeplitz covariance matrix structure instrumental in the FIR analysis also holds for this OE model input representation after a transformation. However, an issue is that the corresponding minimum variance cost function for the OE case will be more complicated than for FIR models, and that the dimension of the optimization problem will be of one degree higher than for the corresponding FIR case. The proposed OE framework is applied to minimum variance input signal design in system identification frequency response estimation and model predictive control. The results are illustrated by numerical examples.