Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

Abstract
In this paper we design a model based method to locate a leakage and estimate its size in a gas network, using a linearised version of an hyperbolic PDE. To do this, the problem is reduced to two identical ODEs, allowing in this way for a representation of the pressure as well as the mass flow in terms of its system of fundamental solutions. Then using the available measurements at the grid boundary points, the correspondent coefficients can be determined. Assuming pressure continuity, we check for consistency of the coefficients in order to find faulty pipelines. Thence, the location of the leakage can be found either graphically or using a numerical method for a specific pipe. Next, its size can also be estimated. © Springer International Publishing Switzerland 2017.

Abstract
In this paper we propose to model a high pressure gas network using a quadripole approach. Although being simple, the proposed model seems to be adequate to network analysis and control, and is directly extendible to more complex networks, i.e., networks with junctions and loops. The model is proven to be liable on a case study constructed from data supplied by REN Gasodutos, where we investigate the effect of a leakage on the mass flow and pressure along the pipeline with the final objective to define a model based methodology for leakage detection and location. © Springer International Publishing Switzerland 2017.

Abstract
System Identification (SI) is a methodology for building mathematical models of dynamic systems from experimental data, i.e., using measurements of the system input/output (IO) signals to estimate the values of adjustable parameters in a given model structure. The process of SI requires some steps, such as measurement of the IO signals of the system in time or frequency domain, selection of a candidate model structure, choice and application of a method to estimate the value of the adjustable parameters in the candidate model structure, validation and evaluation of the estimated model to see if the model is right for the application needs, which should be done preferably with a different set of data, [PS] and [Lj1]. © 2015 Springer International Publishing.

Abstract
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear parameter varying systems driven by general inputs and a white noise time varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. The key to the proposed algorithm is the fact that the bilinear term between the time varying parameter vector and the state vector behaves like a white noise process. Using a linear Kalman filter model, the bilinear term can be efficiently estimated and then used to construct an augmented input vector at each iteration. Since the previous state is known at each iteration, the system becomes linear, which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a linear parameter varying model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality. © 2007 EUCA.

Abstract
In this paper an identification method for statespace LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.