This paper provides a system identification procedure on a fixed-wing aircraft Long-EZ, which is the critical stage of providing accurate aircraft models for autopilot design in the future. Flight test has been carried out by ITPS Canada Ltd. Extensive real flight test data have been utilized for the identification and verification of a linear transfer function model, a nonlinear neural network model, and a block-oriented model consisting of linear and nonlinear elements. Linear transfer function structure has been determined with physical dynamics, and the model parameters have also been identified. Nonlinearity of aircraft dynamics has been identified using a multilayer perceptron (MLP) neural network structure. Flight data has also been utilized to train this MLP structure. Performance comparison results have demonstrated different predicting capabilities of the developed linear, nonlinear, and block-oriented models. The developed block-oriented model shows its predicting capability in a more satisfactory manner.
Time-Domain System Identification for Long-EZ Fixed-Wing Aircraft Based on Flight Test Data
Lect. Notes Electrical Eng.
Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019) ; Kapitel : 83 ; 887-896
04.12.2019
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
System identification , Fixed-wing aircraft , Transfer function , Multilayer perceptron (MLP) neural network , Wiener model Engineering , Control and Systems Theory , Computational Intelligence , Robotics and Automation , Artificial Intelligence , Data-driven Science, Modeling and Theory Building , Power Electronics, Electrical Machines and Networks
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