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一种面向多无人机协同编队控制的改进深度神经网络方法

  • Wenguang Xie
  • , Kang Wu
  • , Fang Yan
  • , Haobin Shi
  • , Xiaocheng Zhang
  • Key Laboratory of Airworthiness Certification Technology for Civil Aviation Aircraft
  • Civil Aviation University of China

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

It is crucial to develop an effective controller for the multi-UAV system to contribute to the frontier fields, such as the electronic warfare. To address the dilemma of the cooperative formation with the high dimensional data, a deep neural network(NN) controller is developed in this paper. Firstly, a deep NN model is used to tune parameters of PID controller online. Secondly, this paper introduces an improved deep NN model integrating the momentum to improve the performance of the classical NN model and satisfy the condition for the real time cooperative formation. Lastly, the cooperative formation task is achieved by extending the proposed cooperative controller with an improved NN to the complex multi-UAV system. The simulation result of multi-UAV formation demonstrates the effectiveness of the proposed method, which achieves a faster formation than competitors.

投稿的翻译标题A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System
源语言繁体中文
页(从-至)295-302
页数8
期刊Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
38
2
DOI
出版状态已出版 - 1 4月 2020

关键词

  • Improved deep neural network
  • Momentum
  • Multi-UAV formation
  • PID controller
  • Simulation

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