An overview of subspace-based system identification methods is presented. Comparison between diferent algorithms are given and similarities pointed out. Abstract-Subspace-based methods for system identification have attracted much attention during the past few years. This interest is due to the ability of providing accurate state-space models for multivariable linear systems directly from input...

The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimization approach allows to include constraints on the unknown matrices in the data-equation characterizing subspace identification methods, such as the lower triangular block-Toeplitz of weighting matrices constructed ...

The purpose of this work is to identify a linear time-invariant dynamic model of wastewater treatment plants with multilevel pseudo random signals as an excitation input. The plants naturally aim to remove suspended substances, organic material and phosphate. An activated sludge process becomes the best technology available to control the discharge of pollutants. For this purpose, state-space m...

In this paper, a closed-loop subspace identification approach through an orthogonal projection and subsequent singular value decomposition is proposed. As a by-product of this development, it explains why some existing subspace methods may deliver a bias in the presence of the feedback control and suggests a remedy to eliminate the bias. Furthermore, as the proposed method is a projection based...

Canal systems are complex nonlinear, distributed parameter systems with changing parameters according to the operating point. In this paper, a linear parameter-varying LPV state-space canal control model is obtained by identification in a local way using a multimodel approach. This LPV identification procedure is based on subspace methods for different operating points of an irrigation canal co...

The application of state-space-based subspace system identification methods to training-based estimation for quasi-static multiinput–multi-output (MIMO) frequency-selective channels is explored with the motivation for better model approximation performance. A modification of the traditional subspace methods is derived to suit the non-contiguous nature of training data in mobile communication sy...

The main objective of this work is to develop a recursive algorithm for identification in the state-space of linear stochastic discrete multivariable non-stationary system; a computational process called MOESP_AOKI_VAR is proposed and implemented to achieve this. The proposed algorithm is based on the subspace methods: Multivariable Output-Error State Space (MOESP), used for computational model...