Data-driven order reduction in Hammerstein–Wiener models of plasma dynamics. (April 2021) - Spinosa, Angelo Giuseppe; Buscarino, Arturo; Fortuna, Luigi; Iafrati, Matteo; Mazzitelli, Giuseppe
Abstract: The problem of identifying and therefore modelling a complex system makes use of various techniques and strategies whose computational efforts change drastically. It is not straightforward to analyse the complexity of a system as a whole because of myriads of factors, such as the way of arranging its constituent items and how they interact mutually. Intuitively, the bigger the set of sub-parts is, the more numerous the degrees of freedom are. Additionally there is not a specific and global criterion for optimally determining an always-working method that makes the identification procedure easier, especially in those contexts where the number of unknown variables can make the difference. In this sense, plasma physics is not an exception, being a field where complex phenomena, such as plasma instabilities, easily arise. From a systemic, high-level perspective, the possibility of employing a model that can describe these behaviours is particularly appealing, since it can be exploited for control applications that have not to neglect the underlying physical nature. So far, most of the work published in literature has focused on more physically-grounded models, which could describe how plasma physics works in detail, but very little has been done as mentioned before, with the aim of providing a computational, yet system-oriented, insight of these physical systems. Starting from real flux measurements recorded thanks to suitable sensors installed inside Tokamak machines, the paper attempts to provide a solution based on already known tools available in literature to solve the aforementioned problem, by combining both machine learning-based strategies for dimensionality reduction and control theory. More in detail, the whole architecture presented in this work is founded on the use of auto-encoders, which are intrinsically capable of compressing input features thanks to their structure, and Hammerstein–Wiener models, which are structurally endowed with both linear and non-linear sub-modellers for better capturing the whole dynamics to identify. By merging these functional blocks, it is possible to address both the issue of establishing the most relevant sub-set of variables for identification and the identification problem itself, resulting in a fully customisable approach to data-driven modelling.