Abstract

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.

Abstract
In view to the decentralised problem of gas network optimisation, we model the problem as differential game where the players are the network controllable elements that communicate through nearest-neighbour network components. The controllable elements are sources and compressors. But since these do not have the same relevance within the network, it will be interesting to use a game hierarchical framework, i.e., to model the network operation as a Stackelberg game. Also, the disturbed version of the same problem suits the problem better because is is assumed that the network works with nominated operational levels. The variations of the real operation can then be viewed as disturbances to these system operational levels.

Abstract
Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test.An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment.A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals. © The Institution of Engineering and Technology 2019.

Abstract
In this paper, the gas dynamics within the pipelines is written as a wave repetitive process, and modified in a way that the dynamics is driven by the boundary conditions. We study controllability of the system through boundary control and every agent, as well as observability of the system being steered by initial and boundary data. Next, we obtain sufficient criteria for the existence and uniqueness of boundary equilibrium controls. From the point of view of some applications, e.g. in high pressure gas pipeline management, it seems to make sense to consider boundary data controls. The same problem is then extended to its infinite counterpart since it may run infinitely and, in this case, we become interested in studying its stabilisation.

Abstract
This article the gas network is modelled as a repetitive process with smoothing and an explicit representation for its solution is presented. By an adequate choice of the inhomogeneity, the system is steered through boundary data control. We state explicit conditions for boundary control of the gas networks as well as observability criteria. Using these notions, some 2D criteria of stability are also applied to gas networks. To finalise, we use a 2D transfer function representation to withdraw a stability criterium suitable to being used in the practice with the use of a computer. © VDE VERLAG GMBH 2013.