| Autor: |
Sreenatha G. Anavatti, Vishwas Puttige |
| Rok vydání: |
2007 |
| Předmět: |
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| Zdroj: |
SMC |
| DOI: |
10.1109/icsmc.2007.4413945 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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