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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| 格式: | Conference or Workshop Item |
| 语言: | English |
| 出版: |
2010
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| 主题: | |
| 在线阅读: | http://eprints.um.edu.my/11328/1/T5-3-2.pdf http://eprints.um.edu.my/11328/ |
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| 总结: | 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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