Volume 38, Number 2, April 2020
|Page(s)||295 - 302|
|Published online||17 July 2020|
A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System
Key Laboratory of Airworthiness Certification Technology for Civil Aviation Aircraft, Tianjin 300300, China
2 College of Airworthiness, Civil Aviation University of China, Tianjin 300300, China
3 College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
4 School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
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.
Key words: multi-UAV formation / improved deep neural network / PID controller / momentum / simulation
关键字 : 协同编队 / 改进深度神经网络 / PID控制器 / 动量因子
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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