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JCP
2008

Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

8 years 11 months ago
Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach
In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to environmental variations. This task of accurate modelling has been achieved with a multi-network architecture. The multi-network with dynamic selection technique allows a combination of online and offline neural network models to be used in the architecture where the most suitable outputs are selected based on a given criterion. The neural network models are based on the autoregressive technique. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. The multi-network dynamic selection technique has been validated on real-time hardware in the loop (HIL) simulation and the results show the superiority in performance ...
Vishwas R. Puttige, Sreenatha G. Anavatti
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JCP
Authors Vishwas R. Puttige, Sreenatha G. Anavatti
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