Feedback control of dynamical systems has benefited from recent development of sensing
and actuation nodes with strong local computation capabilities and from the use of digital …

The subspace approach to state-space modeling offers numerically reliable algorithms for
computing state-space descriptions directly from data. The methods are competitive with …

In this article on-line parameter estimation of dynamical systems is addressed in the context
of model-based networked control systems (MB-NCSs). Stability conditions that are robust to …

In this paper some results concerning the asymptotic distribution of the estimates of the
transfer function obtained by using subspace methods are given. The discussion centers on …

Stochastic subspace identification (SSI) is a process that linearizes the identification
problem by utilizing singular value decomposition (SVD) and QR factorization. This …

This paper addresses least-squares estimation of parameters in digital input/output models
of linear time-invariant distributed systems in the presence of white process and sensor …

This work presents a new class of subspace algorithms for system identification, in which the
projection of future data onto past data is performed by a multichannel least-squares lattice …

Based on the linearization of the structure's vibration equation in the state space, the
stochastic subspace (SSI) approach is often used for system identification in the time domain …

Control systems that transmit information over a shared digital communication network offer
improved efficiency, flexibility, and reliability compared to traditional closed loop feedback …

The purpose of the proposed research is to develop a class of subspace system
identification methods based on RLS lattice filters. This provides an alternative way of doing …