Explainable nonlinear state-space modelling: analysing the GVT of a battery-operated aircraft

Traditionally, ground vibration tests of aircraft are processed using modal analysis algorithms. Most algorithms that are in commercial use today are grounded in the domain of linear system identification. These tools have proven their worth and are still the state-of-the-art in commercial aviation, even though advances have been made in the research community. One of the more promising advances is the ability now to develop fully nonlinear models from experimental data. This capability could drastically improve the quality of information that can be extracted from a ground vibration test experiment. In this work, we utilize recently developed concepts in the nonlinear state-space modeling framework, for instance, a nonlinear state selection method for the polynomial nonlinear state-space models and nonlinear function decoupling for a state-space neural networks. These concepts enable to build more concise, nonlinear, explainable (to some extent), data-driven models. We illustrate the methodology first on an analytical example containing multiple linear modes and one isolated source of nonlinear distortion and compare the performance of classical linear techniques to the new nonlinear modeling framework. Then, we demonstrate this framework on a real-life multiple-input-multiple-output ground vibration test of the Magnus eFusion light sports aircraft.