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Real-time multi-network based identification with dynamic selection implemented for a low cost UAV

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2 Author(s)
Puttige, V.R. ; New South Wales Univ., Canberra ; Anavatti, S.G.

This paper describes a system identification technique based on dynamic selection of multiple neural networks for the Unmanned Aerial Vehicle (UAV). The UAV is a multi- input multi-output (MIMO) nonlinear system. The neural network models are based on the autoregressive technique. The multi-network dynamic selection method allows a combination of online and offline neural network models to be used in the architecture where the most suitable output is selected based on the given criteria. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. Real-time hardware in the loop (HIL) simulation results show that the multi-net dynamic selection technique performs better than the individual models.

Published in:

Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

Date of Conference:

7-10 Oct. 2007