This paper puts forward a PD-type iterative learning control algorithm for a class of discrete
spatially interconnected systems with unstructured uncertainty. By lifting and changing the …

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 …

This paper considers the identification of a network consisting of discrete-time LTI systems
that are interconnected by their unmeasurable states. For a large-scale network, the …

We propose a decentralized subspace algorithm for the identification of large-scale,
interconnected systems that are described by sparse (multi) banded state-space matrices …

In this paper we consider the identification of a linear module that is embedded in a dynamic
network using noisy measurements of the internal variables of the network. This is an …

This manuscript details and extends the system identification methods leveraging the
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …

Stimulated by the enhancing requirements for system safety as well as process reliability in
complex industrial processes, this paper investigates a data‐driven fault detection and fault …

Due to advancing technology, systems in engineering are becoming increasingly complex
and interconnected. Despite the ubiquity of systems that can be modelled as interconnected …

In this paper, fully considering the form of the nonlinear model after linearization at the
steady-state point, an improved subspace identification method based on the steady-state …

In this paper, we consider the identification of matrix state-space models (MSSM) of the
following form: X (k+ 1)= A 2 X (k) A 1 T+ B 2 U (k) B 1 TY (k)= C 2 X (k) C 1 T+ E (k) for all …