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 …

In this article, a gray-box parsimonious subspace identification method using sampled
dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems …

Model predictive control has become a widespread solution in many industrial applications
and is gaining ground in the field of energy management and automation systems of …

Standard subspace methods for the identification of discrete-time, linear, time-invariant
systems are transformed into generalized convex optimization problems in which the poles …

Spurred by the problem of identifying, in real-time, the adhesion levels between the tyre and
the road, a practical, linear parameterisation (LP) model is proposed to represent the tyre …

This paper discusses the problem of system identification when frequency domain side-
information is available. We mainly consider the case where the side-information is provided …

This Article addresses the problem of integrating subspace-based model identification with
first-principles modeling for handling scenarios where the subspace model identifies …

This paper addresses the problem of designing and implementing a data-driven model
based model predictive controller (MPC). In particular, we consider the problem where a …

Subspace identification is a very useful tool for estimating a state-space model for a dynamic
system. However, most of the subspace identification methods (SIMs) can only provide …

In this paper, a prior-knowledge-based subspace identification method (SIM) is proposed for
batch processes subject to repeatable disturbances. The proposed method is a two-step …