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Volume 34 Issue 7
Jul.  2008
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Journal of Beijing University of Aeronautics and Astronautics  > 2008  >  34(7): 861-864.
Liu Jiarun, Shen Gongzhang. Attitude control based on inverse dynamics and online parameter identification[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(02): 111-115. (in Chinese)
Citation: Xi Haijiao, Zhang Xiaolin. Identification of coaxial helicopter dynamic system with elman neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(7): 861-864. (in Chinese)
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Identification of coaxial helicopter dynamic system with elman neural networks

  • School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 13 Sep 2007
  • Publish Date: 31 Jul 2008
    Abstract
  • A modified elman neural network according to the dynamic & kinematic equations of helicopters was established, the training algorithms for the neural networks(NN) was derived, and the training steps and their realizations were provided. From the telemetry data collection of real trial flights of a type of helicopter, the data of typical flying attitudes were exploited to train the NN and calculate the elements of associated weighted matrix, thus the NN for dynamic analysis against the helicopter was obtained. To illustrate the NN effeteness, the vertical operation response in the constant velocity forward flying attitude was simulated, comparing simulation results with real trial flight data shows that the NN basically reflects the real dynamic behavior of the helicopter.

     

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  • [1] 周国仪.共轴式直升机飞行动力学建模及数值模拟 .北京:北京航空航天大学航空科学与工程学院,2003 Zhou Guoyi. Modeling and numerical simulation of thedynamics of coaxial helicopter . Beijing:School of Aeronautic Science and Engineering, Beijing University of Aeronautice and Astronautics, 2003(in Chines) [2] Hornik K, Stinchornbe M, White H. Multilayer feedforward network are universal approximators[J]. Neural Network,1989,2:359-366 [3] 阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005:276-327 Yan Pingfan, Zhang Changshui. Artificial neural networks and evolutionary computing[M]. Beijig:Tshinghua University Press, 2005:276-327(in Chinese) [4] Williams R, Zipser D.A learning algorithm for continually running fully recurrent NN[J]. Neural Computation,1989,1:270-280 [5] Gernasky M. Simple recurrent network trained by RTRL and extended Kalmann filter algorithm[J]. Neural Network World, 2003, 13(3):223-234
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