The Loewner framework has recently been proposed for the system identification of mechanical systems, mitigating limitations current frequency domain fitting processes extraction modal parameters. In this work, computational performance, in terms elapsed time till identification, is assessed. This investigated on a hybrid, numerical and experimental dataset against two well-established methods ...

Closed-loop system identification and fault detection and isolation are the two fundamental building blocks of process monitoring. Efficient and accurate process monitoring increases plant availability and utilisation. This dissertation investigates a subspace system identification and fault detection methodology for the Benfield process, used by Sasol, Synfuels in Secuda, South Africa, to remo...

The paper presents a subspace type of identification method for multivariable linear parameter-varying systems in state space representation with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of systems is the enormous dimensions of the data matrices involved. To overcome the curse of dimensionality, we suggest to use only the most dominant ro...

Port network modeling of physical systems leads directly to an important class of passive state space systems: port-Hamiltonian systems. We consider here methods for model reduction of large scale port-Hamiltonian systems that preserve port-Hamiltonian structure and are capable of yielding reduced order models that satisfy first-order optimality conditions with respect to an H2 system error met...

in this paper, we first discuss about canonical dual of g-frameλp = {λip ∈ b(h, hi) : i ∈ i}, where λ = {λi ∈ b(h, hi) :i ∈ i} is a g-frame for a hilbert space h and p is the orthogonalprojection from h onto a closed subspace m. next, we provethat, if λ = {λi ∈ b(h, hi) : i ∈ i} and θ = {θi ∈ b(k, hi) :i ∈ i} be respective g-frames for non zero hilbert spaces hand k, and λ and θ are unitarily e...

We show that kernel canonical correlation analysis (KCCA) can be used to construct a state sequence of an unknown nonlinear dynamical system from delay vectors of inputs and outputs. In KCCA a feature map transforms the available data into a high dimensional feature space, where classical CCA is applied to find linear relations. The feature map is only implicitly defined through the choice of a...

The problem of MIMO recursive identiication is considered and analyzed within the framework of subspace-based state space system identiication (4SID). With respect to previous contributions to this area, in this paper the use of recent signal processing algorithms for the recursive update of the SVD is proposed. To accommodate for arbitrary correlation of the disturbances, an instrumental varia...

It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic...