Closed-loop output error identification algorithms with predictors based on generalized orthonormal transfer functions: Convergence conditions and bias distribution

This paper proposes an improved version of closed-loop output-error identification algorithms, where the predictor is established on a generalized basis of orthonormal transfer functions. It is shown that the selection of the basis poles impacts the convergence conditions and the bias distribution of the schemes.These algorithms present several advantages: They are able to identify in closed-loop fast sampled systems, stiff systems (with modes spread over three decades or more), and reduced order models. Moreover, they are suitable for unstable systems or controllers. A simulation example shows the effectiveness of this approach. These algorithms can be employed in an open-loop context by using a straightforward simplification.