Optimal input design for the identification of linear multivariable systems with disjoint parametrization

Abstract

This paper considers the problem of generating test signals for parameter estimation in linear multi-input multi-output stochastic systems using a frequency domain approach. The model is disjointly parametrized and the input signals are power-constrained and chosen to be optimal in the sense of maximizing system information where the criterion employed is a scalar function of the inverse Fisher information matrix.

Independence of the input components is shown to be consistent with optimality and this leads to a sufficient condition for local identifiability and a reduction in the size of the design optimization problem.