Design of broadband excitation signals
Perturbation signals for system identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC) - Special issue on trends in system identification
Comparing different approaches to model error modeling in robust identification
Automatica (Journal of IFAC)
Brief Robustness analysis tools for an uncertainty set obtained by prediction error identification
Automatica (Journal of IFAC)
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
On the estimation of transfer functions, regularizations and Gaussian processes-Revisited
Automatica (Journal of IFAC)
System identification for achieving robust performance
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper presents a consistent framework for the quantification of noise and undermodelling errors in transfer function model estimation. We use the, so-called, ''stochastic embedding'' approach, in which both noise and undermodelling errors are treated as stochastic processes. In contrast to previous applications of stochastic embedding, in this paper we represent the undermodelling as a multiplicative error characterised by random walk processes in the frequency domain. The benefit of the present formulation is that it significantly simplifies the estimation of the parameters of the embedded process yielding a closed-form expression for the model error quantification. Simulation and experimental examples illustrate how the random walks effectively capture typical cases of undermodelling found in practice, including underdamped modes. The examples also show how to use the method as a tool in the determination of model order and pole location in fixed denominator model structures.