Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data. (June 2019)

• Record Type: Journal Article
• Authors: Mazhar, Farrukh; Choudhry, Mohammad A; Shehryar, Muhammad
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 7(2019)
• Page Start: 2549
• Page End: 2569
• Publication Date: 2019-06
• Subjects: Airship -- artificial neural network -- dynamic modelling -- lighter than air vehicle -- nonlinear auto-regressive model with exogenous inputs model -- system identification; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410018783131
• Languages: English
• ISSNs: 0954-4100

Abstract

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.