Linear parameter-varying two rotor aero-dynamical system modelling with state-space neural network

Abstract

In every model-based approaches, i.e., fault diagnosis, fuzzy control, robust fault-tolerant control, the exact model is crucial. This paper presents a methodology which allows to obtain an exact model of high-order, non-linear cross-coupled system, namely Two Rotor Aero-dynamical System (TRAS), using a state-space neural network. Moreover, the resulting model is presented in a linear parameter-varying (LPV) form making it easier to analyze (i.e., its stability and controllability) and control. Such a form is obtained by direct transformation of the neural network structure into quasi-LPV model. For the neural network modelling, a SSNN Toolbox is utilized.