Small-scale helicopter system identification model using recurrent neural networks
Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series- Paral...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/11328/1/T5-3-2.pdf http://eprints.um.edu.my/11328/ |
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| Summary: | Designing a reliable flight control for an autonomous
helicopter requires a high performance dynamics model. This
paper studies the recurrent neural network nonlinear model
identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series-
Parallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter. |
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