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Adaptive Neural Network Control of Small Unmanned Aerial Rotorcraft

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Abstract

This paper proposes an online learning adaptive neural network for small unmanned aerial rotorcraft to improve control performance during flight. Based on state error information, the weight matrix of the adaptive neural network can be updated on line by using lyapunov function. Therefore, no prior training data is needed for the training of the adaptive neural network. Combined with feedback control, the adaptive neural network can construct the map between the state error information and disturbances to compensate for system disturbances. The effectiveness of the proposed method is validated by a series of simulations and flight tests. Compared with feedback control method, the adaptive neural network control method can estimate and eliminate disturbances quickly to yield a good tracking performance.

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Authors and Affiliations

  1. School of Instrument Science and Opto-electronic Engineering, Beijing University of Aeronautics and Astronautics, Beijing, China

    Xusheng Lei & Jiancheng Fang

  2. Department of Electrical Engineering, National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

Authors
  1. Xusheng Lei
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  2. Shuzhi Sam Ge
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  3. Jiancheng Fang
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Correspondence to Xusheng Lei.

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Lei, X., Ge, S.S. & Fang, J. Adaptive Neural Network Control of Small Unmanned Aerial Rotorcraft. J Intell Robot Syst 75, 331–341 (2014). https://doi.org/10.1007/s10846-013-0017-2

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  • DOI: https://doi.org/10.1007/s10846-013-0017-2

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