Operational modal analysis deals with the estimation of modal parameters from vibration
data obtained in operational rather than laboratory conditions. This paper extensively …

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 …

Subspace identification for closed loop systems has been recently studied by several
authors. A class of new and consistent closed-loop subspace algorithms is based on …

A crucial aspect in system identification is the assessment of the accuracy of the identified
system matrices. Stochastic Subspace Identification (SSI) is a widely used approach for the …

Weconsider the problem of learning a realization for a linear time-invariant (LTI) dynamical
system from input/output data. Given a single input/output trajectory, we provide finite time …

Data-driven predictive control (DDPC) has been recently proposed as an effective
alternative to traditional model-predictive control (MPC) for its unique features of being time …

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 …

In this paper, we investigate the relation between a recently proposed subspace method
based on predictor identification (PBSID), known also as ldquowhitening filter algorithm …

The uncertainty afflicting modal parameter estimates stems from eg, the finite data length,
unknown, or partly measured inputs and the choice of the identification algorithm …

Subspace identification methods (SIMs) for estimating state-space models have been
proven to be very useful and numerically efficient. They exist in several variants, but all have …