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

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...

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Bibliographic Details
Published in2007 IEEE International Conference on Systems, Man and Cybernetics pp. 759 - 764
Main Authors Puttige, V.R., Anavatti, S.G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Costs
Hardware
MIMO
Neural networks
Nonlinear dynamical systems
Nonlinear systems
System identification
Testing
Unmanned aerial vehicles
Vehicle dynamics
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Summary: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.
ISBN:142440990X
9781424409907
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2007.4413945