This article reviews the development of experiment design in the field of identification of
dynamical systems, from the early work of the seventies on input design for open loop …

This paper traces the strong relations between experimental design and control, such as the
use of optimal inputs to obtain precise parameter estimation in dynamical systems and the …

A key issue in system identification is how to cope with high system complexity. In this
contribution we stress the importance of taking the application into account in order to cope …

In prediction error identification, the information matrix plays a central role. Specifically,
when the system is in the model set, the covariance matrix of the parameter estimates …

This letter presents a new experiment design method for data-driven modeling and control.
The idea is to select inputs online (using past input/output data), leading to desirable rank …

We propose an algorithm to actively estimate the parameters of a linear dynamical system.
Given complete control over the system's input, our algorithm adaptively chooses the inputs …

Abstract Models of inverse systems are commonly encountered in control, eg, feedforward.
The aim of this paper is to address several aspects in identification of inverse models …

Model-based control design plays a key role in today's industrial practice, and industry
demands cuttingedge methods for identifying the necessary models. However, additional …

A key problem in optimal input design is that the solution depends on system parameters to
be identified. In this contribution we provide formal results for convergence and asymptotic …

Data informativity is a crucial property to ensure the consistency of the prediction error
estimate. This property has thus been extensively studied in the open-loop and in the closed …