This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

Many industrial processes belong to distributed parameter systems (DPS) that have strong
spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control …

This article is concerned with the parameter identification problem of nonlinear dynamic
responses for the linear time‐invariant system by means of an impulse excitation signal and …

This paper presents the author's views on the development of identification for control. The
paper reviews the emergence of this subject as a specific topic over the last 15 years, at the …

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 …

This paper develops the idea of min–max robust experiment design for dynamic system
identification. The idea of min–max experiment design has been explored in the statistics …

In this study, the authors present an overview of closed‐loop subspace identification
methods found in the recent literature. Since a significant number of algorithms has …

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 …

A framework for reformulating input design problems in prediction error identification as
convex optimization problems is presented. For linear time-invariant single input/single …

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 …