Conference Paper

Modeling of Nonlinear Enhanced Air Levitation System using NARX Neural Networks

Taha H.A.
Othman M.K.
Abbas N.E.
Sayed Y.K.
Ammr H.H.
Shalaby R.

the proposed paper aims to design and model an air levitation system, which is a highly nonlinear system because of its fast dynamics and low damping. The system is trained using a Nonlinear Autoregressive model with exogenous input (NARX model). An enhanced height measurement system, modified setup, and several training techniques have been used to overcome the restrictions that the non-linearity of the system imposes in the literature. The system mathematical model has been illustrated, followed by an identified model using NARX model trained on several input-output data from the physical setup, which led to perfectly define the unknown parameters of the system. The data is collected using a closed-loop identification implemented using a Python-Arduino-linked GUI vision system, and the results were remarkable when compared to the literature setup. The results verify that NARX neural network, with suitable training procedures, could successfully model a real air levitation system. © 2021 IEEE.