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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 paper, we analyze the finite sample complexity of stochastic system identification
using modern tools from machine learning and statistics. An unknown discrete-time linear …

Part I deals with the mathematical preliminaries: numerical linear algebra; system theory;
stochastic processes; and Kalman filtering. Part II explains realization theory as applied to …

A typical design procedure for model predictive control or control performance monitoring
consists of: identification of a parametric or nonparametric model; derivation of the output …

This book is intended to be a treatise on the theory and modeling of secondorder stationary
processes with an exposition of some application areas which we believe are important in …

Principal component analysis (PCA) has been widely used for monitoring complex industrial
processes with multiple variables and diagnosing process and sensor faults. The objective …

In this article, a unified identification framework called constrained subspace method for
structured state-space models (COSMOS) is presented, where the structure is defined by a …

In this thesis, new system identication methods are presented for three particular types of
nonlinear systems: linear parameter-varying state-space systems, bilinear state-space …

A novel subspace identification method is presented which is able to reconstruct the
deterministic part of a multivariable state-space LPV system with affine parameter …